The associations of physical activity, sedentary time, and sleep with V˙O2max in trained and untrained children and adolescents: A novel five-part compositional analysis

Adam Runacres; Kelly A. MacKintosh; Sebastien Chastin; Melitta A. McNarry

doi:10.1371/journal.pone.0275557

Abstract

The benefits of physical activity (PA) and the negative impacts of sedentary time (SED) on both short- and long-term health in youth are well established. However, uncertainty remains about how PA and SED jointly influence maximal oxygen uptake (). Therefore, the aim of this study was to determine the joint influence of PA and SED on using compositional analyses. 176 adolescents (84 girls, 13.8 ± 1.8 years) completed an incremental ramp test and supramaximal validation bout on a cycle ergometer, with PA and SED recorded for seven consecutive days on the right hip using a ActiGraph GT3X accelerometer. Time spent in Sleep, SED, light, moderate and vigorous PA was analysed using a compositional linear regression model. Compositions with 10 minutes more time in vigorous PA (> 27.5 mins⋅day^-1) compared to the average 17.5 mins⋅day^-1 were associated with a + 2.9% - 11.1% higher absolute and scaled whilst compositions with less (> -10 mins⋅day^-1) VPA were associated with a reduced absolute and allometrically scaled (-4.6% - 24.4%). All associations were irrespective of sex, maturity, and training status. The proportion of time spent sedentary had little impact on absolute and scaled (0.01–1.98%). These findings therefore highlight that intensity of PA may be of greater importance for increases in than reductions in SED and should be incorporated into future intervention designs.

Citation: Runacres A, MacKintosh KA, Chastin S, McNarry MA (2023) The associations of physical activity, sedentary time, and sleep with V˙O_2max in trained and untrained children and adolescents: A novel five-part compositional analysis. PLoS ONE 18(3): e0275557. https://doi.org/10.1371/journal.pone.0275557

Editor: Zulkarnain Jaafar, Universiti Malaya, MALAYSIA

Received: September 19, 2022; Accepted: February 22, 2023; Published: March 8, 2023

Copyright: © 2023 Runacres et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and Supporting Information files.

Funding: This work was supported by the Knowledge Economy Skills Scholarships (KESS). KESS is a Pan-Wales higher-level skills initiative led by Bangor University on behalf of the HE sector in Wales. It is part funded by the Welsh Government’s European Social Fund (ESF) convergence programme for West Wales and the Valleys.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Poor maximal oxygen uptake () has been associated with an increased risk of cardiovascular and metabolic disease, leading to an increased likelihood of premature mortality across the lifespan [1–4]. , defined as the highest rate of oxygen consumption despite further increases in work rate [5], is key to athletic performance, with youth athletes consistently reported to have a greater than their untrained counterparts [6–9]. However, the term in the paediatric literature is contentious as only 20–40% of children and adolescents display a plateau, and thus a supramaximal validation bout is necessary to validate a maximal effort [8, 9]. Consequently, the term peak will be used within this study where a supramaximal bout was not undertaken and used only when a scientifically rigorous supramaximal validation bout was employed. Whilst training is well-established to improve in youth athletes [6, 9–11], what remains less clear is the influence of habitual physical activity (PA) and sedentary time (SED) on . More specifically, some studies have reported a significant association between PA and peak [12–14], whereas others argue that children and adolescents rarely experience PA of a sufficient duration and intensity to significantly influence peak [11, 15].

Moderate-to-vigorous PA (MVPA) is perhaps the most widely used PA metric in children and adolescents [16–18] and remains the focus of governmental physical activity guidelines [19, 20]. Research in children has consistently shown that increased levels of MVPA are positively associated with peak [12–14], whilst excess SED is negatively correlated with peak [21, 22]. However, what remains unclear is the joint association between VPA and SED as some elite junior athletes may demonstrate similar, if not more, SED than their inactive, sedentary peers [23]. Therefore, whether VPA can offset the negative effects of SED requires urgent investigation, but this cannot be done with a continued reliance on correlational statistics or the use of linear regression modelling which assumes independence between variables [23, 24]. Indeed, traditional correlational statistics are inappropriate to account for the constrained and co-dependent nature of PA data, potentially creating spurious associations.

Compositional analysis allows all movement behaviours to be expressed as a proportion of a finite period, enabling the individual, and joint, effects of movement behaviours on outcome variables to be established [16–18, 25, 26]. Consequently, compositional analysis could provide novel insights into the concomitant influences of movement behaviours, intensity, and volume on . Indeed, Carson et al. [16] found that the overall movement behaviour composition explained ~38% of the variance in the peak of 4,169 Canadian children and adolescents (8–17 years). Despite this, when 10 minutes of time were allocated to, or removed from, MVPA, there was negligible effect on peak , with predicted changes ranging from 0.03–0.05% [16]. This could be due, at least in part, to methodological considerations, including the estimation of peak from a field-based test which is likely to misrepresent true cardiorespiratory fitness [27], the pooling of data from boys and girls despite the well-established physiological differences [28], and the failure to account for maturity or training status. Furthermore, Carson et al [16] only explored the effect of changing MVPA compositions thereby combining MPA and VPA, which may potentially mask the importance of the intensity of physical activity for improving peak in youth. Indeed, training studies consistently show that significant improvements in absolute, and allometrically scaled, peak only occur when the intensity is sufficiently vigorous [10, 29]. Similarly, Gutin et al. [30] reported a stronger association between VPA and peak (r² = 0.43, p < 0.01) than MPA (r² = 0.30, p < 0.01) in adolescents, findings corroborated by both Dencker et al. [12] and Latt et al. [14] who reported that the amount of time spent in VPA explained 9.0–15.8% of the variance in peak in children and adolescents. However, none of these studies appropriately accounted for body mass differences, with Latt et al. [14] and Dencker et al. [13] utilising ratio scaled peak , and Dencker et al. [12] using theoretical exponents to allometrically scale their peak data. By not fully accounting for body mass, heavier, more mature individuals may be penalised, creating spurious associations [15]. Hence, whether these results are a consequence of physiological pathways, or methodological inconsistencies, remains to be fully established.

Therefore, the aim of this study was to examine the independent, and interactive, effects of the five movement behaviours (SED, light intensity PA (LPA), MPA, VPA and Sleep) on in children and adolescents. The second aim was to explore the effect of baseline fitness, sex, and maturity on the predicted changes in elicited by changing PA compositions.

2. Methods

Ethics approval was granted by the institutional research ethics committee at Swansea University prior to the commencement of data collection and the study conformed to the Declaration of Helsinki. Before participants were accepted into this cross-sectional study, written informed parental consent and participant assent were obtained, along with all parents completing a pre-screening medical questionnaire on behalf of their child. Participants were excluded if they had known cardiovascular, metabolic, kidney, or any other disease that meant they would not have been able to complete the exercise protocol. The trained children and adolescents were all national level athletes who were part of a long-term athlete development (LTAD) program overseen by the national governing body (NGB) of their sport (Hockey, Football and Gymnastics). Untrained participants were recruited from local schools across South Wales and were not formally engaged in sport training outside of curricular physical education lessons. The final sample consisted of 237 participants, encompassing 108 trained (43 girls; age: 13.5 ± 2.1 years) and 129 untrained (51 girls; 13.8 ± 1.4 years) children and adolescents.

2.1 Experimental procedures

All participants were required to attend one session at which they initially had their stature and sitting height measured to the nearest 0.1 cm using a Holtain Stadiometer (Holtain, Crymych, Dyfed, UK) and their body mass measured to the nearest 0.1 kg using electronic scales (Seca 803, Seca, Chino, CA, USA). Maturity status was subsequently estimated using the equations of Mirwald et al. [31], with participants deemed pre-pubertal, pubertal, and post-pubertal if they were more than one year from, within one year of, or more than one year post peak height velocity (PHV), respectively.

was assessed using an incremental ramp test to volitional exhaustion on a cycle ergometer (Lode Excalibur Sport, Groningen, Netherlands) which started with a three-minute warm-up at 10 W before increasing by 20–25 W⋅min^-1, depending on the participant’s age. All participants were instructed to maintain a cadence of 60–80 revolutions per minute (rpm) throughout the test, with volitional exhaustion defined as when participants could not maintain a cadence above 50 rpm. Inspired and expired air were measured on a breath-by-breath basis throughout the incremental ramp test using a Vyntus metabolic cart (VYAIRE medical Ltd, Mettawa, IL, USA). Following five minutes active and ten minutes passive rest, a supramaximal validation bout was performed [32]. Specifically, participants warmed up for a further three minutes at 10 W before a step-transition to 105% of the peak power achieved during the incremental ramp test. Participants were instructed to maintain a cadence above 50 rpm for as long as possible, with gas exchange measured continuously on a breath-by-breath basis throughout the exercise bout.

Participant’s habitual physical activity was subsequently measured for seven consecutive days using a ActiGraph GT3X (ActiGraph, Pensacola, Florida, USA) worn on the right hip, sampling at 100 Hz. Children and adolescents also completed a seven-day log to detail periods when the monitor was removed, waking time and time going to bed, to minimise the misclassification of non-wear time as sedentary time or Sleep.

2.2 Data analyses

The raw breath-by-breath data from both the and supramaximal bout were averaged into 10-second bins, with the defined as the highest 10-second moving average during the ramp incremental or the supramaximal test. To aid comparisons between sex, maturity, and training sub-groups, was allometrically scaled (scaled ) to account for body mass differences between participants [6, 32]. Evenson et al. [33] cut-points were utilised to determine the time spent in each PA intensity which have been shown to be the reliable for children and adolescents [34]. Sleep time and efficiency were calculated using the algorithms of Sadeh et al. [35]. Wear-time criteria was set as ≥ 8 hours on any three days. Using the Evenson et al. [33] cut-points, sleep algorithms, and wear-time, each day was expressed as a five-part movement composition (SED, LPA, MPA, VPA, Sleep) and linear predictive models were employed to predict changes in . The smallest worthwhile change (SWC) in (l⋅min^-1) and scaled (ml⋅kg^-b⋅min^-1) was calculated for each sex, maturity and training sub-group using the formula 0.2*group SD [36]. The SWC was then subsequently presented as a percentage of the group mean to aid comparisons between all sub-groups.

All compositional analyses were conducted in R (http://cran.r-project.org) using the compositions package (version 1.40–2) and its dependencies [25]. Compositional geometric means were computed to indicate the proportion of time spent in each PA behaviour or Sleep each day, by expressing each behaviour, after normalisation, as a proportion of the total time [18, 25]. Variance matrices were calculated to provide an indication as to the dispersion and co-dependency of movement behaviours and were calculated by measuring the variance between pair-wise log ratios [25, 26]. Specifically, a ratio tending towards zero indicates high co-dependency, with the numbers further from zero indicating less co-dependency. Sequential linear regression models were created by rotating each of the five behaviours via isometric log ratio (ILR) transformations to examine the relative effect of all movement behaviours on the and scaled [18, 25]. The first coefficient and its p value were reported for each rotation to determine whether the individual movement behaviour was associated with the outcome variable relative to the other movement behaviours, and its relative significance. Additionally, the overall model significance (p value) and R² value were reported to gain an insight into the variance explained by the overall movement composition. All movement behaviours were also sequentially mapped against each other, producing ternary heat maps displaying the predicted absolute and scaled for each sex, training, and maturity group. Finally, change matrices were conducted to predict the change in absolute and scaled by systematically reallocating 10 minutes from one movement behaviour to another [18, 25, 26]. All predictive changes were presented as a percentage change relative to the compositional mean, with significant changes identified as any change greater than the SWC (%).

2.3 Statistical analyses

All traditional statistical analyses were conducted in SPSS version 26 (IBM, Portsmouth, UK), with significance accepted as p < 0.05. Between group differences in anthropometric characteristics and absolute and scaled were assessed using a MANOVA, with post-hoc tests with Bonferroni correction applied to identify the specific location of significant differences as appropriate.

3. Results

Of the original 237 participants, 61 were excluded for failing to meet the wear-time criteria, therefore 84 trained (40 girls) and 92 untrained (44 girls) children and adolescents were included in the final analyses. There were no significant differences in the anthropometrics of those included and excluded (p > 0.05). Post-pubertal adolescents were significantly older, taller, heavier, and more mature than the pubertal or pre-pubertal children (p < 0.01), with significant differences in the same parameters also evident between pubertal adolescents and pre-pubertal children (p < 0.05, Table 1). The trained children and adolescents were taller (F_(1,175) = 12.7, p < 0.01) and had a higher (l⋅min^-1) than their untrained counterparts (F_{(1, 175)} = 15.3, p < 0.01), which persisted even after allometric scaling (F_(1,175) = 18.7, p < 0.01). Overall, boys had a higher absolute and scaled than their female counterparts, irrespective of training or maturity status (F_(1,175) = 19.7, p < 0.01). increased with maturity, irrespective of sex and training status (F_(1,175) = 16.2, p < 0.01), but there was no significant difference between any maturity group for scaled . There were no significant training, sex, or maturity interactions for any anthropometric variable or , regardless of how it was expressed.

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Table 1. Participant descriptives.

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3.1 Physical activity composition description

In the trained participants, the geometric means highlight that the largest portion of the day was spent in SED (41.2%), followed by Sleep (39.2%), with VPA only accounting for 1.6% of the day (Table 2). Similarly, untrained children spent the longest period of the day in SED (44.1%) and Sleep (40.0%), with VPA making up just 1.3% of the day. Trained athletes completed more LPA (F_(1.175) = 38.1, p < 0.01) and VPA (F_(1,175) = 18.6, p < 0.01), but spent significantly less time in Sleep (F_(1,175) = 3.8, p = 0.05) compared to untrained participants, irrespective of sex or maturity. LPA and Sleep, and SED and LPA, demonstrated the smallest variation and therefore highest co-dependency, whereas VPA had the largest pair-wise log ratio variances compared to all other PA behaviours, indicating less co-dependency (Table 3). The ILR model revealed that the overall PA composition significantly predicted both and scaled (Table 4), explaining 48.7% and 37.7%, respectively. However, when individual movements were considered in isolation, the only significant predictor of scaled was VPA (Y_VPA = 6.91, p < 0.02), with no significant individual associations evident for absolute (Table 4).

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Table 2. Geometric means for the whole sample.

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Table 3. Pair-wise log ratio variation matrix in the full sample.

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Table 4. Model ILR parameters for

and scaled

.

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3.2 Impact of PA composition on

Compositions with 10 minutes difference in SED, LPA, MPA, or Sleep had a minimal effect on absolute or scaled in trained children and adolescents (Table 5), with all changes smaller than the SWC (S1 Table), irrespective of training, sex, or maturity status. However, when co-varying for sex, maturity, and training status, compositions with 10 minutes more time in VPA (> 27.5 mins⋅day^-1) compared to the average 17.5 mins⋅day^-1 were associated with a +2.9% - 11.1% higher absolute and scaled and compositions with less than 10 mins⋅day^-1 of VPA were associated with a reduced absolute and scaled (-4.6% - 24.4%). The proportion of SED, LPA, and Sleep had little impact on absolute and scaled (0.01–1.98%). Consequently, VPA was the most influential PA behaviour for absolute, and scaled , irrespective of training status and sex, but the influence of PA behaviours was less clear in pubertal and post-pubertal adolescents (S1 and S2 Figs).

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Table 5. Change matrices of reallocating 10 minutes from the behaviour in columns to the behaviour in the rows on

(l⋅min^-1) and scaled

(ml⋅kg^-b⋅min^-1) in trained children and adolescents, presented as percentage change.

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Contrastingly, in pre-pubertal untrained children, compositions with 10 minutes less VPA (-5.2 mins∙day^-1) compared to the average 15.2 mins∙day^-1 were associated with a decrease in of between 3.2–10.5% (Table 6). Additionally, compositions with less LPA, but more MPA or VPA, were associated with an increase in between 5.2% and 5.8% in pre-pubertal untrained girls.

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Table 6. Change matrices of reallocating 10 minutes from the behaviour in columns to the behaviour in the rows on

(l⋅min^-1) and scaled

(ml⋅kg^-b⋅min^-1) in untrained children and adolescents, presented as percentage change.

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Scaled significantly decreased, irrespective of sex, maturity, or training status, when 10 minutes of VPA was reallocated to any other behaviour (Tables 5 and 6). Moreover, scaled tended to increase when time spent in other movement behaviours was reallocated to VPA. In isolation, both SED and Sleep were not significant predictors of absolute or scaled (Table 4) and displacing SED with any other behaviour had a negligible effect on (0.01–1.98%; Tables 5 and 6) unless it was displaced with VPA where it was associated with an increase of 2.68–6.38% in scaled only. Similarly, compositions with 10 minutes less Sleep and 10 minutes more VPA were associated with a 2.85–6.31% greater scaled , irrespective of sex, training, and maturation. The effects of reallocating time to LPA, SED, or Sleep on were negligible, regardless of how was expressed and irrespective of sex, maturity, or training status.

4. Discussion

This is the first study to examine the inter-related effects of various movement behaviours (SED, LPA, MPA, VPA and Sleep), using a five-part compositional analysis, on absolute and scaled in trained and untrained children and adolescents. The main findings of the present study were that allocating time to, and removing time from, VPA significantly increased and decreased scaled , respectively, regardless of sex, training, or maturity status. Moreover, this study suggests that VPA is potentially 2.4–4.7% more potent in eliciting an improvement in VO_2max over a 10-minute period in children and adolescents. These findings therefore highlight that intensity of PA may be of paramount importance in determining , especially in girls.

Engaging in 10 minutes more VPA, irrespective of which behaviour it displaces, significantly increases both absolute and scaled , regardless of training status. Moreover, of importance, untrained children were predicted to have a larger magnitude of change for the same 10-minute reallocation, in accord with the review of McNarry & Jones [8] which concluded that baseline fitness significantly impacts the magnitude of change experienced following a given stimulus. Furthermore, Mahon [37] reported that 52% of the inter-individual variation in participant’s responses to a training stimulus can be explained by baseline peak . The findings of the current study are, however, discordant with Carson et al. [16] who reported no significant differences when reallocating time to, or from, any movement behaviour. Such discrepancies may be explained by the use of a proxy measure of and not accounting for maturation or training status in the earlier study, which are critical when assessing cardiorespiratory fitness in children and adolescents [10, 11, 27].

The present study supports the notion that children and adolescents may require a vigorous stimulus to significantly improve absolute and scaled [8, 10, 12–14]. Whilst increasing levels of VPA is relatively time efficient, addressing a commonly cited barrier to physical activity [38], it is pertinent to note that the current findings suggest children and adolescents may need to increase their time spent in VPA by over 50%. Considering the limited success at increasing PA in many interventions to date [38, 39], and the small magnitude of increases in VPA reported even in those considered successful [40], the current findings highlight the need to drastically change our approach to PA promotion. These findings could be speculated to support the contention suggested by many authors that HIIT may represent an important public health intervention tool [29, 41].

Dencker et al. [12] reported weak, but significant, correlations between VPA and peak (r² = 0.32) and scaled peak (r² = 0.27). Furthermore, a recent review of the relationship between PA and peak in youth concluded that, despite decades of research, there was still no consensus [11]. These equivocal findings may be related to a reliance on techniques that fail to account for the inter-related and inherently constrained nature of PA behaviours, leading to spurious conclusions [18, 25, 26]. Moreover, the reliance on ratio scaled peak potentially creates erroneous associations [15, 42]. Of note, in the present study when was allometrically scaled by body mass, the overall PA composition explained ~11% less variance compared to absolute . This may be due, at least in part, to physically active children having a higher lean body mass (LBM) than their sedentary counterparts [43], indicating that differences in body composition may also be critical when determining the effect of re-allocating PA. Nevertheless, the PA composition still explained 37.7% of the variance in allometrically scaled , demonstrating the powerful influence of habitual PA on aerobic fitness.

The finding that allocating time to MPA decreased scaled was surprising. These associations could be to the high amount of VPA undertaken by the trained group within this study and, consequently, if time is removed and replaced with MPA, it will have a negative impact on . Future research is required to explore the interaction of MPA and VPA in other athletic populations to confirm this hypothesis. Of note, the fixed time reallocation used within compositional analysis studies to date [18, 20], and in the present study, may over-estimate the magnitude of change in a given variable. More specifically, a 10-minute change in VPA constituted a ~50% increase in VPA within the current sample but the same 10-minute reallocation only represented a 1.9% increase in SED time. Therefore, a greater insight into the independent, and interactive, effects of movement behaviours on may be gained by investigating the effects of the same percentage change in movement behaviours on . Nevertheless, evidence is emerging that the intensity of PA may be critical in improving both performance and health-related parameters in paediatric populations [18, 20, 25] and thus VPA should be encouraged, as opposed to MPA, to engender the greatest long-term health benefits.

Sedentary time in isolation was not a significant predictor of either absolute or scaled in children and adolescents within the current study, irrespective of sex, maturation, or training status. This is in direct contrast to the growing body of literature suggesting that excess sedentary time could lead to a decreased [21, 22] and suggests that instead of SED being the problem per se, it is potentially the PA behaviour it replaces that is influential. More specifically, significant increases in were only associated with compositions where the amount of time spent sedentary decreased and was replaced with VPA. However, whilst displacing SED with LPA and MPA were not associated with an increased , if these behavioural changes were introduced as part of a wider health initiative, they could still contribute to improving the health of the nation.

Future research should seek to implement targeted interventions informed by compositional analyses to ascertain the required duration needed to elicit the changes predicted. This is of particular importance as a plethora of research has investigated the influence of different training methodologies on both absolute and scaled , with their effectiveness being reviewed elsewhere [8, 10]. One major issue with many paediatric training studies to date is the lack of accounting for changes in habitual PA levels across the intervention period, and this could help explain the equivocal findings of some intervention types [10, 43].

Whilst there are numerous strengths associated with this study, such as the use of a novel five-part compositional analysis approach, allometrically scaling , and accounting for training, maturity and sex differences, there are limitations which must be acknowledged. Firstly, a relatively low wear-time criteria was set of any three days with at least eight hours of wear-time; a more stringent wear-time criteria could potentially influence the relationships established between PA metrics and . Nevertheless, this wear-time has been validated in a paediatric population [34] and was used to maximise participant inclusion within the study. Secondly, the cross-sectional study does not allow the duration over which the habitual changes need to be maintained to observe the associated changes to be elucidated. Thirdly, the small sample size compared to other studies of this type [18, 25] and the representativity of this population needs to be considered when interpreting the results of this study. Finally, the applicability of cycle derived to habitual PA levels is contentious, and therefore future research should endeavour to establish using treadmills to maximise specificity, and to establish whether these findings persist.

5. Conclusion

In conclusion, the proportion of time VPA is a significant predictor of scaled in children and adolescents, independent of training, sex, or maturity status and even when the proportion of time spent in other behaviours is considered. Moreover, reallocating time from VPA in pre-pubertal children predicts a reduced absolute , potentially highlighting the importance of promoting VPA in pre-pubertal children. Future research should seek to establish the duration of targeted PA interventions needed to elicit the significant changes predicted from compositional analyses and report the individual levels of MPA and VPA to ascertain the relative importance of VPA for current, and future, health in children and adolescents.

Supporting information

S1 Table. Smallest worthwhile change in all sub-groups for (l⋅min-1) and allometrically scaled (ml⋅kg^-b⋅min^-1).

SWC = Smallest Worthwhile change, SWC (%) = Smallest worthwhile change as a percentage of the individual group mean.

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(TIF)

S1 Fig. Ternary heat plots for 24-hour movement behaviours and predicted .

Ternary heat plots of all PA behaviours with expected values for all sub-groups with a) trained athletes; b) untrained controls; c) all boys; d) all girls; e) pre-pubertal children; f) pubertal adolescents; and g) post-pubertal adolescents,

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(TIF)

S2 Fig. Ternary heat plots for 24-hour movement behaviours and predicted allometrically scaled .

Ternary plots of all PA behaviours with expected scaled values for all sub-groups with a) trained athletes; b) untrained controls; c) all boys; d) all girls; e) pre-pubertal children; f) pubertal adolescents; and g) post-pubertal adolescent.

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(TIF)

S1 Data. The data used to run the compositional analyses and produce the outcomes for this manuscript are included as supplementary material.

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(XLSX)

Acknowledgments

The authors would like to thank Dr Rachel Hughes and Tim Evans for their help with the design and recruitment for the study. The authors would also like to thank the children and young people and their parents / guardians for taking their time to complete this study.

References

1. Hallal P., Anderson l., Bull F., Guthold R., Haskell W. & Ekelund U. Global physical activity levels: surveillance progress. pitfalls, and prospects. The Lancet. 2012;380, 247–257.
- View Article
- Google Scholar
2. Lee I. M., Hseih C. C. & Paffenbarger R. S. JR Exercise intensity and longevity in men. The Harvard Alumni Health Study. JAMA. 1995;273, 1179–1184. pmid:7707624
- View Article
- PubMed/NCBI
- Google Scholar
3. Paffenbarger R. S. & Lee I. M. A natural history of athleticism, health and longevity. J. Sports Sci. 1998:16, S31–S45.
- View Article
- Google Scholar
4. Laukkanen J. A., Zaccardi F., Khan H., Kurl S., Jae S. Y. & Rauramaa R. Long-term Change in Cardiorespiratory Fitness and All-Cause Mortality: A Population-Based Follow-up Study. Mayo Clin Proc. 2016:91, 1183–8. pmid:27444976
- View Article
- PubMed/NCBI
- Google Scholar
5. Hill A. & Lupton H. Muscular exercise, lactic acid, and the supply and utilsation of oxygen. QJM. 1923:16, 135–171.
- View Article
- Google Scholar
6. Armstrong N. Commentary: Pediatric Aerobic Fitness and Trainability. Pediatr. Exerc. Sci, 2017:29, 8–13.
- View Article
- Google Scholar
7. McNarry M., Welshman J. & Jones A. Influence of training and maturity status on the cardiopulmonary responses to ramp incremental cycle and upper body exercise in girls. J. Appl. Physiol. 2011:110, 375–381. pmid:21088206
- View Article
- PubMed/NCBI
- Google Scholar
8. McNarry M. & Jones A. The influence of training status on the aerobic and anaerobic responses to exercise in children: A review. Eur J Sport Sci. 2014:14, 57–68. pmid:24444245
- View Article
- PubMed/NCBI
- Google Scholar
9. Carazo-Vargas P. & Moncada-Jiménez J. A meta-analysis on the effects of exercise training on the VO2 max in children and adolescents. Retos. 2015:27, 184–187.
- View Article
- Google Scholar
10. Baquet G., Van Praagh E. & Berthoin S. Endurance training and aerobic fitness in young people. Sports Med. 2003 33, 1127–1143. pmid:14719981
- View Article
- PubMed/NCBI
- Google Scholar
11. Armstrong N. & Barker A. Endurance Training and Elite Young Athletes. In: Armstong N. & Mcmanus A. (eds.) The Elite Young Athlete. Basel, Karger: Medicine in Sports Science. 2011.
12. Dencker M., Thorsson O., Karlsson M., Lindén C., Svensson J., Wollmer P. & Andersen L. 2006. Daily physical activity and its relation to aerobic fitness in children aged 8–11 years. Eur. J. Appl. Physiol. 2006:96.
- View Article
- Google Scholar
13. Dencker M., Thorsson O., Karlsson M., Lindén C., Eiberg S., Wollmer P. & Andersen L. Gender differences and determinants of aerobic fitness in children aged 8–11 years. Eur. J. Appl. Physiol. 2007:99.
- View Article
- Google Scholar
14. Latt E., Maestu J., Raask T., Rubin D., Purge P., Saar M., Utsal L., Jurimae J., Maasalu K. & Jurimae T. Association of physical activity to cardiovascular fitness and fatness in 12–13 year old boys in different weight status. J. Public Health. 2013:21, 231–239.
- View Article
- Google Scholar
15. Armstrong N. & Welsman J. Youth cardiorespiratory fitness: evidence, myths and misconceptions. Bull World Health Organ. 2019. pmid:31673193
- View Article
- PubMed/NCBI
- Google Scholar
16. Carson V., Tremblay M., Chaput J. & Chastin S. Associations Between Sleep Duration, Sedentary Time, Physical Activity, and Health Indicators Among Canadian Children and Youth Using Compositional Analyses. Appl Physiol Nutr Metab. 2016: 41. pmid:27306435
- View Article
- PubMed/NCBI
- Google Scholar
17. Lynch B. A., Kaufman T. K., Rajjo T. I., Mohammed K., Kumar S., et al. Accuracy of Accelerometers for Measuring Physical Activity and Levels of Sedentary Behaviour in Children: A Systematic Review. J Prim Care Community Health. 2019:10, 1–8.
- View Article
- Google Scholar
18. Carson V., Tremblay M., Chaput J., Mcgregor D. & Chastin S. Compositional analyses of the associations between sedentary time, different intensities of physical activity, and cardiometaolic biomarkers among children and youth from the United States. PLoS One. 2019:14, e0220009.
- View Article
- Google Scholar
19. Chief Medical Officer. UK Chief Medical Officers’ Physical Activity Guidelines. In: CARE D. O. H. A. S. (ed.). 2019.
- View Article
- Google Scholar
20. WHO 2020. WHO Guidelines on Physical Activity and Sedentary Behaviour.
- View Article
- Google Scholar
21. Leitzmann M. F., Jochem C. & Schmid D. Sedentary Behaviour Epidemiology, Springer International. 2018.
- View Article
- Google Scholar
22. Ku P., Steptoe A., Liao Y., Hsueh M. & Chen L. A cut-off of daily sedentary time and all cause mortality in adults: a meta-regression analysis involving more than one million participants. BMC Medicine. 201:16, 1–9.
- View Article
- Google Scholar
23. Exel J, Mateus N, Travassos B, Gonçalves B, Gomes I, Leite N, Sampaio J. Off-training levels of physical activity and sedentary behavior in young athletes: Preliminary results during a typical week. Sports. 2018:6(4),141. pmid:30404165
- View Article
- PubMed/NCBI
- Google Scholar
24. Diaz K., Howard V., Hutto B., Colabianchi N., Vena J. et al. Patterns of Sedentary Behavior and Mortality in U.S. Middle-Aged and Older Adults: A National Cohort Study. Ann. Intern. Med. 2017:167. pmid:28892811
- View Article
- PubMed/NCBI
- Google Scholar
25. Chastin S., Palarea-Albaladejo J., Dontje M. & Skelton D. Combined Effects of Time Spent in Physical Activity, Sedentary Behaviors and Sleep on Obesity and Cardio-Metabolic Health Markers: A Novel Compositional Data Analysis Approach. PloS one. 2015:10. pmid:26461112
- View Article
- PubMed/NCBI
- Google Scholar
26. Dumuid D., Stanford T., Martin-Fernández J., Pedišić Ž., Maher C. et al. Compositional data analysis for physical activity, sedentary time and Sleep research. Stat Methods Med Res. 2018:27. pmid:28555522
- View Article
- PubMed/NCBI
- Google Scholar
27. Armstrong N. & Welsman J. Scientific Rigour in the Assessment and Interpretation of Youth Cardiopulmonary Fitness: A Response to the Paper ‘Normative Reference Values and International Comparisons for the 20-Metre Shuttle Run Test: Analysis of 69,960 Test Results among Chinese Children and Youth. J. Sports Sci. Med. 2020:19, 627–629.
- View Article
- Google Scholar
28. Rogol A. Androgens and puberty. Mol Cell Endocrinol. 2002:198, 25–9. pmid:12573811
- View Article
- PubMed/NCBI
- Google Scholar
29. Cao M., Quan M. & Zhuang J. Effect of High-Intensity Interval Training versus Moderate-Intensity Continuous Training on Cardiorespiratory Fitness in Children and Adolescents: A Meta-Analysis. Int J Environ Res Public Health. 2019:16, 1533. pmid:31052205
- View Article
- PubMed/NCBI
- Google Scholar
30. Gutin B., Yin Z., Humphries M. & Barbeau P. Relations of moderate and vigorous physical activity to fitness and fatness in adolescents. AJCN. 2005:81, 746–750. pmid:15817847
- View Article
- PubMed/NCBI
- Google Scholar
31. Mirwald R. L., Baxter-Jones A. D. G., Bailey D. A. & Beunen G. P. An assessment of maturity from anthropometric measurements. Med Sci Sports Exerc. 2002:34, 689–694. pmid:11932580
- View Article
- PubMed/NCBI
- Google Scholar
32. Barker A. R., Williams C. A., Jones A. M. & Armstrong N. Establishing maximal oxygen uptake in young people during a ramp cycle test to exhaustion. Br. J. Sports Med. 2011:bjsports63180 pmid:19679577
- View Article
- PubMed/NCBI
- Google Scholar
33. Evenson K. R., Catellier D. J., Gill K. I., Ondrak K. S. & Mcmurray R. G. Calibration of two objective measures of physical activity for children. J. Sports Sci. 2008:26, 1557–1565.
- View Article
- Google Scholar
34. Trost S. State of the Art Reviews: Measurement of Physical Activity in Children and Adolescents. Am. J. Lifestyle Med. 2016:1, 299–314.
- View Article
- Google Scholar
35. Sadeh A., Sharkey K. & Carskadon M. Activity-based Sleep-wake identification: an empirical test of methodological issues. Sleep. 1994:17, 201–207. pmid:7939118
- View Article
- PubMed/NCBI
- Google Scholar
36. Hopkins W., Marshall S., Batterham A. & Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009:41, 3–12. pmid:19092709
- View Article
- PubMed/NCBI
- Google Scholar
37. Mahon A. Aerobic Training. In: Armstrong N. & Van Mechelen W. (eds.) Paediatric Exercise Science and Medicine. Oxford University Press. 2008
38. Mannocci A., D’egidio V., Backhaus I., Federici A., Sinopoli A. et al. Are there effective interventions to increase physical activity in children and young people? An Umbrella Review. Int. J. Environ. Res. Public Health. 2020:17, 3528.
- View Article
- Google Scholar
39. Love R., Adams J. & Van Sluijs E. Are school-based physical activity interventions effective and equitable? A meta-analysis of cluster randomized controlled trials with accelerometer-assessed activity. Obes. Rev. 2019:20, 859–870. pmid:30628172
- View Article
- PubMed/NCBI
- Google Scholar
40. Goode A., Hall K., Batch B., Huffman K., Hastings S. et al. The Impact of Interventions that Integrate Accelerometers on Physical Activity and Weight Loss: A Systematic Review. Ann. Behav. Med. 2017:51, 79–93. pmid:27565168
- View Article
- PubMed/NCBI
- Google Scholar
41. Runacres A., Mackintosh K.A., & McNarry M.A. The effect of constant-intensity endurance training and high-intensity interval training on aerobic and anaerobic parameters in youth, J. Sports Sci. 2019:37:21, 2492–2498
- View Article
- Google Scholar
42. Runacres A, Mackintosh K, Evans T, McNarry MA. Effects of Sex, Training, and Maturity Status on the Cardiopulmonary and Muscle Deoxygenation Responses during Incremental Ramp Exercise. International Journal of Environmental Research and Public Health. 2022; 19(12):7410 pmid:35742656
- View Article
- PubMed/NCBI
- Google Scholar
43. Bitar A., Vernet J., Coudert J. & Vermorel M. Longitudinal changes in body composition, physical capacities and energy expenditure in boys and girls during the onset of puberty. Eur J Nutr. 2000:39, 157–63. pmid:11079735
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Hallal P., Anderson l., Bull F., Guthold R., Haskell W. & Ekelund U. Global physical activity levels: surveillance progress. pitfalls, and prospects. The Lancet. 2012;380, 247–257.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Lee I. M., Hseih C. C. & Paffenbarger R. S. JR Exercise intensity and longevity in men. The Harvard Alumni Health Study. JAMA. 1995;273, 1179–1184. pmid:7707624
View Article
PubMed/NCBI
Google Scholar

[5] View Article

[6] PubMed/NCBI

[7] Google Scholar

[ref3] 3. Paffenbarger R. S. & Lee I. M. A natural history of athleticism, health and longevity. J. Sports Sci. 1998:16, S31–S45.
View Article
Google Scholar

[9] View Article

[10] Google Scholar

[ref4] 4. Laukkanen J. A., Zaccardi F., Khan H., Kurl S., Jae S. Y. & Rauramaa R. Long-term Change in Cardiorespiratory Fitness and All-Cause Mortality: A Population-Based Follow-up Study. Mayo Clin Proc. 2016:91, 1183–8. pmid:27444976
View Article
PubMed/NCBI
Google Scholar

[12] View Article

[13] PubMed/NCBI

[14] Google Scholar

[ref5] 5. Hill A. & Lupton H. Muscular exercise, lactic acid, and the supply and utilsation of oxygen. QJM. 1923:16, 135–171.
View Article
Google Scholar

[16] View Article

[17] Google Scholar

[ref6] 6. Armstrong N. Commentary: Pediatric Aerobic Fitness and Trainability. Pediatr. Exerc. Sci, 2017:29, 8–13.
View Article
Google Scholar

[19] View Article

[20] Google Scholar

[ref7] 7. McNarry M., Welshman J. & Jones A. Influence of training and maturity status on the cardiopulmonary responses to ramp incremental cycle and upper body exercise in girls. J. Appl. Physiol. 2011:110, 375–381. pmid:21088206
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref8] 8. McNarry M. & Jones A. The influence of training status on the aerobic and anaerobic responses to exercise in children: A review. Eur J Sport Sci. 2014:14, 57–68. pmid:24444245
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref9] 9. Carazo-Vargas P. & Moncada-Jiménez J. A meta-analysis on the effects of exercise training on the VO2 max in children and adolescents. Retos. 2015:27, 184–187.
View Article
Google Scholar

[30] View Article

[31] Google Scholar

[ref10] 10. Baquet G., Van Praagh E. & Berthoin S. Endurance training and aerobic fitness in young people. Sports Med. 2003 33, 1127–1143. pmid:14719981
View Article
PubMed/NCBI
Google Scholar

[33] View Article

[34] PubMed/NCBI

[35] Google Scholar

[ref11] 11. Armstrong N. & Barker A. Endurance Training and Elite Young Athletes. In: Armstong N. & Mcmanus A. (eds.) The Elite Young Athlete. Basel, Karger: Medicine in Sports Science. 2011.

[ref12] 12. Dencker M., Thorsson O., Karlsson M., Lindén C., Svensson J., Wollmer P. & Andersen L. 2006. Daily physical activity and its relation to aerobic fitness in children aged 8–11 years. Eur. J. Appl. Physiol. 2006:96.
View Article
Google Scholar

[38] View Article

[39] Google Scholar

[ref13] 13. Dencker M., Thorsson O., Karlsson M., Lindén C., Eiberg S., Wollmer P. & Andersen L. Gender differences and determinants of aerobic fitness in children aged 8–11 years. Eur. J. Appl. Physiol. 2007:99.
View Article
Google Scholar

[41] View Article

[42] Google Scholar

[ref14] 14. Latt E., Maestu J., Raask T., Rubin D., Purge P., Saar M., Utsal L., Jurimae J., Maasalu K. & Jurimae T. Association of physical activity to cardiovascular fitness and fatness in 12–13 year old boys in different weight status. J. Public Health. 2013:21, 231–239.
View Article
Google Scholar

[44] View Article

[45] Google Scholar

[ref15] 15. Armstrong N. & Welsman J. Youth cardiorespiratory fitness: evidence, myths and misconceptions. Bull World Health Organ. 2019. pmid:31673193
View Article
PubMed/NCBI
Google Scholar

[47] View Article

[48] PubMed/NCBI

[49] Google Scholar

[ref16] 16. Carson V., Tremblay M., Chaput J. & Chastin S. Associations Between Sleep Duration, Sedentary Time, Physical Activity, and Health Indicators Among Canadian Children and Youth Using Compositional Analyses. Appl Physiol Nutr Metab. 2016: 41. pmid:27306435
View Article
PubMed/NCBI
Google Scholar

[51] View Article

[52] PubMed/NCBI

[53] Google Scholar

[ref17] 17. Lynch B. A., Kaufman T. K., Rajjo T. I., Mohammed K., Kumar S., et al. Accuracy of Accelerometers for Measuring Physical Activity and Levels of Sedentary Behaviour in Children: A Systematic Review. J Prim Care Community Health. 2019:10, 1–8.
View Article
Google Scholar

[55] View Article

[56] Google Scholar

[ref18] 18. Carson V., Tremblay M., Chaput J., Mcgregor D. & Chastin S. Compositional analyses of the associations between sedentary time, different intensities of physical activity, and cardiometaolic biomarkers among children and youth from the United States. PLoS One. 2019:14, e0220009.
View Article
Google Scholar

[58] View Article

[59] Google Scholar

[ref19] 19. Chief Medical Officer. UK Chief Medical Officers’ Physical Activity Guidelines. In: CARE D. O. H. A. S. (ed.). 2019.
View Article
Google Scholar

[61] View Article

[62] Google Scholar

[ref20] 20. WHO 2020. WHO Guidelines on Physical Activity and Sedentary Behaviour.
View Article
Google Scholar

[64] View Article

[65] Google Scholar

[ref21] 21. Leitzmann M. F., Jochem C. & Schmid D. Sedentary Behaviour Epidemiology, Springer International. 2018.
View Article
Google Scholar

[67] View Article

[68] Google Scholar

[ref22] 22. Ku P., Steptoe A., Liao Y., Hsueh M. & Chen L. A cut-off of daily sedentary time and all cause mortality in adults: a meta-regression analysis involving more than one million participants. BMC Medicine. 201:16, 1–9.
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref23] 23. Exel J, Mateus N, Travassos B, Gonçalves B, Gomes I, Leite N, Sampaio J. Off-training levels of physical activity and sedentary behavior in young athletes: Preliminary results during a typical week. Sports. 2018:6(4),141. pmid:30404165
View Article
PubMed/NCBI
Google Scholar

[73] View Article

[74] PubMed/NCBI

[75] Google Scholar

[ref24] 24. Diaz K., Howard V., Hutto B., Colabianchi N., Vena J. et al. Patterns of Sedentary Behavior and Mortality in U.S. Middle-Aged and Older Adults: A National Cohort Study. Ann. Intern. Med. 2017:167. pmid:28892811
View Article
PubMed/NCBI
Google Scholar

[77] View Article

[78] PubMed/NCBI

[79] Google Scholar

[ref25] 25. Chastin S., Palarea-Albaladejo J., Dontje M. & Skelton D. Combined Effects of Time Spent in Physical Activity, Sedentary Behaviors and Sleep on Obesity and Cardio-Metabolic Health Markers: A Novel Compositional Data Analysis Approach. PloS one. 2015:10. pmid:26461112
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref26] 26. Dumuid D., Stanford T., Martin-Fernández J., Pedišić Ž., Maher C. et al. Compositional data analysis for physical activity, sedentary time and Sleep research. Stat Methods Med Res. 2018:27. pmid:28555522
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref27] 27. Armstrong N. & Welsman J. Scientific Rigour in the Assessment and Interpretation of Youth Cardiopulmonary Fitness: A Response to the Paper ‘Normative Reference Values and International Comparisons for the 20-Metre Shuttle Run Test: Analysis of 69,960 Test Results among Chinese Children and Youth. J. Sports Sci. Med. 2020:19, 627–629.
View Article
Google Scholar

[89] View Article

[90] Google Scholar

[ref28] 28. Rogol A. Androgens and puberty. Mol Cell Endocrinol. 2002:198, 25–9. pmid:12573811
View Article
PubMed/NCBI
Google Scholar

[92] View Article

[93] PubMed/NCBI

[94] Google Scholar

[ref29] 29. Cao M., Quan M. & Zhuang J. Effect of High-Intensity Interval Training versus Moderate-Intensity Continuous Training on Cardiorespiratory Fitness in Children and Adolescents: A Meta-Analysis. Int J Environ Res Public Health. 2019:16, 1533. pmid:31052205
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref30] 30. Gutin B., Yin Z., Humphries M. & Barbeau P. Relations of moderate and vigorous physical activity to fitness and fatness in adolescents. AJCN. 2005:81, 746–750. pmid:15817847
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref31] 31. Mirwald R. L., Baxter-Jones A. D. G., Bailey D. A. & Beunen G. P. An assessment of maturity from anthropometric measurements. Med Sci Sports Exerc. 2002:34, 689–694. pmid:11932580
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref32] 32. Barker A. R., Williams C. A., Jones A. M. & Armstrong N. Establishing maximal oxygen uptake in young people during a ramp cycle test to exhaustion. Br. J. Sports Med. 2011:bjsports63180 pmid:19679577
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref33] 33. Evenson K. R., Catellier D. J., Gill K. I., Ondrak K. S. & Mcmurray R. G. Calibration of two objective measures of physical activity for children. J. Sports Sci. 2008:26, 1557–1565.
View Article
Google Scholar

[112] View Article

[113] Google Scholar

[ref34] 34. Trost S. State of the Art Reviews: Measurement of Physical Activity in Children and Adolescents. Am. J. Lifestyle Med. 2016:1, 299–314.
View Article
Google Scholar

[115] View Article

[116] Google Scholar

[ref35] 35. Sadeh A., Sharkey K. & Carskadon M. Activity-based Sleep-wake identification: an empirical test of methodological issues. Sleep. 1994:17, 201–207. pmid:7939118
View Article
PubMed/NCBI
Google Scholar

[118] View Article

[119] PubMed/NCBI

[120] Google Scholar

[ref36] 36. Hopkins W., Marshall S., Batterham A. & Hanin J. Progressive statistics for studies in sports medicine and exercise science. Med Sci Sports Exerc. 2009:41, 3–12. pmid:19092709
View Article
PubMed/NCBI
Google Scholar

[122] View Article

[123] PubMed/NCBI

[124] Google Scholar

[ref37] 37. Mahon A. Aerobic Training. In: Armstrong N. & Van Mechelen W. (eds.) Paediatric Exercise Science and Medicine. Oxford University Press. 2008

[ref38] 38. Mannocci A., D’egidio V., Backhaus I., Federici A., Sinopoli A. et al. Are there effective interventions to increase physical activity in children and young people? An Umbrella Review. Int. J. Environ. Res. Public Health. 2020:17, 3528.
View Article
Google Scholar

[127] View Article

[128] Google Scholar

[ref39] 39. Love R., Adams J. & Van Sluijs E. Are school-based physical activity interventions effective and equitable? A meta-analysis of cluster randomized controlled trials with accelerometer-assessed activity. Obes. Rev. 2019:20, 859–870. pmid:30628172
View Article
PubMed/NCBI
Google Scholar

[130] View Article

[131] PubMed/NCBI

[132] Google Scholar

[ref40] 40. Goode A., Hall K., Batch B., Huffman K., Hastings S. et al. The Impact of Interventions that Integrate Accelerometers on Physical Activity and Weight Loss: A Systematic Review. Ann. Behav. Med. 2017:51, 79–93. pmid:27565168
View Article
PubMed/NCBI
Google Scholar

[134] View Article

[135] PubMed/NCBI

[136] Google Scholar

[ref41] 41. Runacres A., Mackintosh K.A., & McNarry M.A. The effect of constant-intensity endurance training and high-intensity interval training on aerobic and anaerobic parameters in youth, J. Sports Sci. 2019:37:21, 2492–2498
View Article
Google Scholar

[138] View Article

[139] Google Scholar

[ref42] 42. Runacres A, Mackintosh K, Evans T, McNarry MA. Effects of Sex, Training, and Maturity Status on the Cardiopulmonary and Muscle Deoxygenation Responses during Incremental Ramp Exercise. International Journal of Environmental Research and Public Health. 2022; 19(12):7410 pmid:35742656
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref43] 43. Bitar A., Vernet J., Coudert J. & Vermorel M. Longitudinal changes in body composition, physical capacities and energy expenditure in boys and girls during the onset of puberty. Eur J Nutr. 2000:39, 157–63. pmid:11079735
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

Figures

Abstract

1. Introduction

2. Methods

2.1 Experimental procedures

2.2 Data analyses

2.3 Statistical analyses

3. Results

3.1 Physical activity composition description

3.2 Impact of PA composition on

4. Discussion

5. Conclusion

Supporting information

S1 Table. Smallest worthwhile change in all sub-groups for (l⋅min-1) and allometrically scaled (ml⋅kg-b⋅min-1).

S1 Fig. Ternary heat plots for 24-hour movement behaviours and predicted .

S2 Fig. Ternary heat plots for 24-hour movement behaviours and predicted allometrically scaled .

S1 Data. The data used to run the compositional analyses and produce the outcomes for this manuscript are included as supplementary material.

Acknowledgments

References

S1 Table. Smallest worthwhile change in all sub-groups for (l⋅min-1) and allometrically scaled (ml⋅kg^-b⋅min^-1).