OBJECTIVES

There is no clear guidance on the intensity and duration of physical activity (PA) that adolescents require to maximise cardiorespiratory fitness (CRF). We aimed to determine the strength of associations between each PA intensity and CRF, independently of other intensities, and the PA duration at each intensity associated with maximal CRF.

METHODS

PA and CRF were assessed in 339 adolescents aged 13 to 14 years by wrist-worn accelerometers and 20-m shuttle runs, respectively. Partial regression modeling was used to construct residualized PA variables at each PA intensity that were uncorrelated with each other. Moving average models were optimally fitted to determine relationships between residualized PA variables and CRF. Threshold regression models determined the duration of PA above which CRF improvement was minimal.

RESULTS

Greater vigorous PA (VPA) was associated with better CRF until about 20 minutes of daily VPA, when the relationship plateaued. Moderate and light PA, and sedentary time were not associated with CRF in partial models. Adolescents performing 14 (range 12–17) minutes of daily VPA had median CRF. Participants in the upper quartile of VPA had 1.03 z-scores higher CRF than those in the lowest quartile (95% confidence interval: 0.75 to 1.30).

CONCLUSIONS

Our data suggest that 20 minutes of daily VPA may be best for maximizing CRF in adolescence. As moderate-to-vigorous PA guidelines can be satisfied by only undertaking moderate PA, with no apparent independent benefit, we suggest that future guidelines focus on VPA alone, simplifying public health messaging.

What’s Known on This Subject:

Cardiorespiratory fitness (CRF) is an important determinant of health that can be improved by physical activity (PA). However, there is no clear guidance on the intensity and duration of PA intensities that adolescents need to achieve maximal CRF.

What This Study Adds:

This study shows that vigorous PA is associated with CRF in adolescents, until about 20 minutes when the relationship plateaued, whereas moderate PA is not, suggesting that this duration of vigorous PA may be enough to change CRF.

The health benefits of physical activity (PA; movement through skeletal muscle action, increasing energy expenditure) and cardiorespiratory fitness (CRF; individual capacity for PA measured against performance standards) are well known, including reduced risks of obesity, diabetes, hypertension, cardiovascular disease (CVD), poor mental health, and all-cause mortality.15  Improving CRF in young people is an important goal, given that adolescents and young adults with high CRF have a lower risk of developing CVD risk factors compared with their low CRF counterparts.69  Measures of PA and CRF are often used interchangeably in relation to CVD risk and their strong interdependency makes it difficult to determine whether it is one or both of these factors that is important for cardiovascular health.10,11  Nevertheless, interventions that increase both are likely to be beneficial.

Although genetic variation contributes to differences in CRF,12,13  the principal modifiable determinant of CRF is habitual PA. PA programs within schools provide evidence that increasing the amount of daily vigorous PA (VPA) increases CRF in children,14  and cross-sectional analyses have shown that VPA is more strongly associated with CRF than moderate PA (MPA).15  Although data in young people are limited, adult studies have shown that regular, brief VPA is highly effective at improving health markers, including CRF,16  which is also an important marker of health in youth.17  VPA may be more practical and achievable than MPA, as it has been proposed that adults would have to perform >927 minutes per week of MPA compared with only >40 minutes of VPA to reduce CVD risk.1  Importantly, none of these prior studies have attempted to account for the correlation between different PA intensities. Therefore, although existing evidence suggests that increasing habitual VPA is likely to yield the greatest benefit for CRF in adolescents, it is uncertain if this is truly the most beneficial exercise intensity, whether other intensities offer additional independent benefits for CRF, and how much PA at each intensity is required to achieve adequate CRF according to established norms.

The World Health Organization (WHO) recently published updated PA guidelines for all age groups, and recommend that children and adolescents undertake moderate-to-vigorous PA (MVPA) for an average of 60 minutes per day to improve physical, mental, and cognitive health.18  Despite their recommendation, 81% of adolescents failed to achieve these minimum activity levels in 2016.19  Although the guidelines were based on several systematic reviews, the method by which the expert panel arrived at this specific PA target for children and adolescents was not specified.

Given the worsening CRF of children and adolescents,2022  recommendations about the intensity and duration of PA required to reverse this trend are urgently needed and should be based on robust evidence. As guidelines are currently based on combined MVPA targets, we consider this an important aim. This study set out to establish the extent to which each PA intensity is associated with CRF, independently of other PA intensities, and the duration of activity at each intensity associated with maximum CRF.

A cross-sectional study of deidentified data collected during the prescreening process for the Oxfordshire Sedentariness, Obesity, and Cardiometabolic Risk in Adolescents: A Trial of Exercise in Schools study (NCT04118543) was undertaken. The study was approved by The University of Oxford Ethics Committee (Ref. R54302/RE006), was in accordance with the Declaration of Helsinki, and was written in accordance with Strengthening the Reporting of Observational Studies in Epidemiology guidelines.23 

Participants aged 13 to 14 years from two secondary schools in Oxfordshire, United Kingdom were studied in the 2018 to 2019 and 2019 to 2020 academic years. The schools informed families about the study and the opportunity was given for their children to be opted-out. Data were collected during physical education lessons run by the school, with support from the study team. Anonymous data on fitness indicators were collected to characterize the year-group. Adolescents who were opted-out participated in the physical education lesson but their data were not collected.

Height to the nearest 0.1 cm and weight to the nearest 0.1 kg were measured using a portable Harpenden Stadiometer (Holtain Ltd, Crymych, UK) and a SECA medical 770 digital floor scale (SECA, Hamberg, Germany), respectively. Body mass index (BMI) was calculated using height and weight and was additionally reported as age- and sex-adjusted z-scores, using WHO 2007 reference data.24 

CRF was measured using the 20 m shuttle run test (20mSRT; starting at 8 km/h, then 9 km/h, then increasing by 0.5 km/h at each subsequent stage) and recorded as the total number of completed shuttles.25  Sex-specific CRF z-scores were calculated for all participants based on our sample of boys (n = 143) and girls (n = 124) with normal-weight (BMI z-score equal to or greater than −2 and less than +1).

Adolescents were provided with a small triaxial, wrist-worn accelerometer (AX3, Axivity Ltd., Melton Park, United Kingdom) and were asked to wear this for 7 consecutive days. Wrist-worn accelerometery has been shown to accurately predict children’s energy expenditure26,27  and the AX3 accelerometer has been shown to accurately detect various child postures and PA intensities,28,29  as validated by its successful use in large pediatric studies.30  A valid recording consisted of at least 3 weekdays and 1 weekend day. Each valid day required >6 hours of awake wear time (>50% of awake time for most participants). We repeated the analyses using a criterion of >10 hours of awake wear time to ensure that this threshold did not alter the results. Sleep periods were not included in analyses. To define the noise floor of stationary AX3 sensors, we placed accelerometers on a flat surface, with no interference for 24 hours. From this, we defined nonwear as periods where the standard deviation (SD) and range of g for 1-second epochs varied by <0.018 g and <0.11 g, respectively, for at least 15 minutes.31  Nonwear periods were excluded from analyses. Although an algorithmic process was used initially to identify day and night periods and nonwear time, all data were then screened manually and edited if necessary.

The sampling frequency was set to 100 Hz with a dynamic range of ±16 g. A bespoke MatLab script calibrated the three axes using an established method (Supplemental Information).32  After calibration, the signal was filtered using a fourth order Butterworth bandpass filter (high 0.2 Hz and low 15 Hz). Euclidean norm minus 1 (ENMO) was then calculated and summed into one second epochs. This metric of PA is referred to as bandpass-filtered followed by Euclidean norm (BFEN). Previously validated thresholds for BFEN were used to determine the average duration of daily PA at each intensity: 0.1 g for light (LPA), 0.314 g for MPA, and 0.998 g for VPA.33  PA below the LPA threshold was categorized as sedentary time (ST).34 

Other metrics of PA processing were calculated as described previously and PA bands were defined using published thresholds relevant to each metric.31  The metrics were ENMOz (ENMO with negative values rounded to 0; thresholds: LPA, 0.1 g; MPA, 0.192 g; and VPA, 0.696 g),35,36  HFEN (a fourth order Butterworth high-pass filter [0.2 Hz] applied to each of the three axes followed by EN; thresholds: LPA, 0.1 g; MPA, 0.314 g; and VPA, 0.998 g),34  HFEN+ (HFEN plus ENMO of the three axes after a fourth order Butterworth low-pass filter [0.2 Hz]; thresholds as per HFEN). As there is a significant body of literature summarizing PA as ActiGraph counts, we also translated our signals in ActiGraph counts for comparability, using a validated method.37 

Statistical analysis was completed using Stata (version 16.1, StataCorp, College Station, Texas). Participants with data on sex, BMI z-score, 20mSRT, and a valid recording of PA were included. Histogram plots were used to assess normality of variables. Differences in the number of boys and girls were assessed using χ2 tests. Sex differences were tested using independent t tests or Mann-Whitney U test and represented as mean ± SD or median (interquartile range), depending on distributions. Differences between PA metrics were assessed by analysis of variance and Bonferroni post hoc test or Kruskal-Wallis H tests and Dunn’s post hoc test, depending on distributions.

An individual’s PA at each intensity is strongly correlated with their PA at other intensities (Supplemental Table 3), preventing robust comparison of the effects of different intensities on health outcomes. This can be addressed by constructing variables that contain only the variance at each intensity that is independent of all other intensities (ie, zero-correlated, Supplemental Table 4). We did this using partial multivariable linear regression modeling38  to generate a set of residualized variables for MPA, LPA, and ST, which together with their base variable (unadjusted VPA), are referred to as rPA variables (Supplemental Information). To check that the directionality of this partial modeling did not significantly influence the results, we repeated this process in the opposite direction, starting with ST as the base variable, to generate another set of rPA variables. Standard multivariable regression with all the normal PA intensities was also used to support the findings of the partial models.

We tested the association of each rPA variable with CRF, adjusting for sex, in separate models and, given their mutually independent nature, in a fully adjusted model including all rPA variables. These results were compared with models using normal PA variables.

To capture the underlying relationships between rPA and CRF, we used a filtering mechanism to smooth the raw data, removing any “noise,” and allowing the trends in our data to be revealed. This approach differs from more common statistical methods such as linear regression that fit a function with a predefined shape to the data. By contrast, we used a zero-phase moving-average filter, which makes no assumption about the shape of the relationship and imposes no linearity constraint, to determine relationships between each rPA variable and CRF. The Akaike information criterion (AIC) was used to determine the moving average window size that yielded the optimum goodness-of-fit of each model before overfitting occurred. The threshold at which CRF might no longer improve significantly with increasing PA was determined as the point where any positive increase began to level off. Further details are provided in Supplemental Information. Comparison was made with linear, second order polynomial, and moving median models to demonstrate which approach best fitted the data. The duration of PA needed to achieve median CRF was determined as the point where the moving average model reached a CRF z-score of 0.

To support the results of the moving average model, sex-specific mean differences in VPA between participants ≤50th percentile (group 1) and participants >50th percentile (group 2) of CRF were assessed using independent t tests. A range of CRF percentile thresholds were determined from our own 20mSRT data, and from the mean 20mSRT data from European and global reference ranges of 13- and 14-year-olds, for comparison.39,40 

To assess differences in CRF between low and high amounts of PA, the upper and lower quartiles of each normal PA variable and of each rPA variable were assessed using independent t tests. Statistical significance was set at P < .05.

Of the 480 adolescents who participated in the health assessment, 339 had sufficient, valid data allowing for inclusion (Fig 1). Their key characteristics are reported in Table 1. Height, weight, BMI, and BMI z-score data were missing for 2, 5, 5, and 6 participants, respectively. Boys were taller than girls, but there were no sex differences in weight, BMI, or BMI z-score. Boys performed better in the 20mSRT and did more VPA. Girls had higher MPA, LPA, and ST measures. PA determined by other metrics are reported in Supplemental Table 5. Adolescents that did not have valid PA wear time had similar characteristics, but had lower z-score total lap results, compared with those with valid PA (Supplemental Table 6). Although not directly assessed, schools covered a range of socioeconomic and ethnic groups (Supplemental Information).

FIGURE 1.

Flow-diagram of the number of adolescents included in the study.a A valid week of physical activity consisted of at least 3 weekdays and 1 weekend day with each valid day requiring >6 hours of wear time.

FIGURE 1.

Flow-diagram of the number of adolescents included in the study.a A valid week of physical activity consisted of at least 3 weekdays and 1 weekend day with each valid day requiring >6 hours of wear time.

Close modal
TABLE 1

Participant Characteristics

MeasureAll participantsGirlsBoysP
N (%) 339 (100) 169 (50) 170 (50) .99 
Height (cm)a 165.2 ± 8.7 162.7 ± 6.9 167.6 ± 9.6 <.001 
Wt (kg)a 56.0 ± 12.3 55.2 ± 11.5 56.9 ± 12.9 .21 
BMI (kg/m2)a 20.4 ± 3.8 20.8 ± 3.8 20.1 ± 3.7 .10 
BMI z-score (WHO)a 0.3 ± 1.2 0.3 ± 1.2 0.3 ± 1.2 .68 
20mSRT (total laps)b,c 44 (32 to 62) 38 (27 to 54) 54 (38 to 76) <.001 
20mSRT (z-score total laps)b,c −0.4 (−1.0 to 0.5) −0.4 (−0.9 to 0.5) −0.4 (−1.1 to 0.5) .99 
Physical activity     
 VPA (mins)b,c 10.6 (7.2 to 15.7) 9.6 (6.6 to 14.1) 11.4 (8.0 to 17.1) .002 
 MPA (mins)a 153.2 ± 42.9 165.8 ± 40.5 140.6 ± 41.7 <.001 
 LPA (mins)b,c 202.5 (166.3 to 228.8) 205.1 (177.0 to 231.2) 198.6 (156.6 to 226.0) .03 
 Sedentary time (mins)a 470.1 ± 99.5 489.9 ± 86.3 450.4 ± 107.7 <.001 
MeasureAll participantsGirlsBoysP
N (%) 339 (100) 169 (50) 170 (50) .99 
Height (cm)a 165.2 ± 8.7 162.7 ± 6.9 167.6 ± 9.6 <.001 
Wt (kg)a 56.0 ± 12.3 55.2 ± 11.5 56.9 ± 12.9 .21 
BMI (kg/m2)a 20.4 ± 3.8 20.8 ± 3.8 20.1 ± 3.7 .10 
BMI z-score (WHO)a 0.3 ± 1.2 0.3 ± 1.2 0.3 ± 1.2 .68 
20mSRT (total laps)b,c 44 (32 to 62) 38 (27 to 54) 54 (38 to 76) <.001 
20mSRT (z-score total laps)b,c −0.4 (−1.0 to 0.5) −0.4 (−0.9 to 0.5) −0.4 (−1.1 to 0.5) .99 
Physical activity     
 VPA (mins)b,c 10.6 (7.2 to 15.7) 9.6 (6.6 to 14.1) 11.4 (8.0 to 17.1) .002 
 MPA (mins)a 153.2 ± 42.9 165.8 ± 40.5 140.6 ± 41.7 <.001 
 LPA (mins)b,c 202.5 (166.3 to 228.8) 205.1 (177.0 to 231.2) 198.6 (156.6 to 226.0) .03 
 Sedentary time (mins)a 470.1 ± 99.5 489.9 ± 86.3 450.4 ± 107.7 <.001 

Physical activity and sedentary time based on BFEN.

a

Mean ± SD.

b

Median (interquartile range).

c

Non-parametric variable.

Given the sex differences in PA shown in Table 1, all linear models were adjusted for sex. Linear regression of each PA intensity with the CRF measures (total laps and total laps z-score) demonstrated the strongest association with VPA and progressively weaker associations with MPA and LPA (Supplemental Table 7, Model 1). Standard multivariable regression showed that only VPA was independently associated with CRF after adjustment for the other intensities (Supplemental Table 7, Model 2). Further modeling using partial regression was then conducted to examine the independent effects of each intensity. This was done with the rPA variables individually (Supplemental Table 7, Model 3) and in mutually adjusted models with the rPA variables calculated with either VPA (Supplemental Table 7, Model 4) or ST (Supplemental Table 7, Model 5) as the base variable. Only VPA was independently associated with CRF, irrespective of analysis approach. Having thus demonstrated the benefit of the partial regression modeling approach, only rPA variables were used in subsequent analyses.

In the moving average model, greater average daily VPA was associated with better CRF until 19 minutes of VPA, when the relationship plateaued (r2 = 0.35; Fig 2). Median CRF was found in adolescents performing 14 (range 12–17) minutes of VPA daily. The range was estimated as the VPA values where the 95% confidence limits of CRF z-scores crossed the median. Results did not change meaningfully when the awake wear time threshold was set at >10 hours (n = 302; plateau = 20 minutes; range 12–17; r2 = 0.36). As in the linear models, other rPA measures were unrelated to CRF (Fig 3). Moving average models using other metrics of PA demonstrated similar results to BFEN (Supplemental Figs 4-8). The moving average model provided the best fit of the data when compared with linear, second order polynomial, and moving median models (Supplemental Fig 9). When analyses were repeated by sex, there were steep increases in CRF with increasing VPA up to 20 and 18 minutes in boys and girls, respectively, plateauing after that (Supplemental Fig 10). However, these models were not statistically different from each other (overlapping confidence limits).

FIGURE 2.

Moving average model of the association of daily VPA with CRF z-score. The black line indicates the nonlinear relationship with 95% confidence intervals (green). The vertical gray dashed line indicates the plateau. The mean difference in CRF between the lowest (red) and highest (blue) quartiles of VPA is provided.

FIGURE 2.

Moving average model of the association of daily VPA with CRF z-score. The black line indicates the nonlinear relationship with 95% confidence intervals (green). The vertical gray dashed line indicates the plateau. The mean difference in CRF between the lowest (red) and highest (blue) quartiles of VPA is provided.

Close modal
FIGURE 3.

Moving average models of the associations of residualized physical activity variables with CRF. Physical activity variables were adjusted by partial multivariable regression modeling for the confounding effect of correlations between activity levels. rLPA, residualized light physical activity; rMPA, residualized moderate physical activity; rST, residualized sedentary time.

FIGURE 3.

Moving average models of the associations of residualized physical activity variables with CRF. Physical activity variables were adjusted by partial multivariable regression modeling for the confounding effect of correlations between activity levels. rLPA, residualized light physical activity; rMPA, residualized moderate physical activity; rST, residualized sedentary time.

Close modal

To support the findings of the moving average model, independent t tests assessed mean differences in VPA between individuals below (group 1) and above (group 2) a number of different median CRF thresholds (Table 2). Boys and girls in group 2 undertook significantly more VPA than those in group 1, regardless of threshold definition. There were no significant group differences in any of the other rPA intensities. Similarly, CRF z-score differed when comparing the upper and lower quartiles of VPA, but not those of any other rPA measure (1.03 z-scores, 95% CI = 0.75 to 1.30; Figs 2 and 3).

TABLE 2

Sex-Specific Difference in VPA Between Participants Below Versus Above Median Fitness

CRF Median ThresholdGroup 1 VPA (mins)Group 2 VPA (mins)Δ (95% CI)P
Boys     
 Total laps 10.7 ± 6.7 15.9 ± 8.3 5.3 (3.0 to 7.5) <.001 
 Z-score total laps 10.8 ± 6.6 17.6 ± 8.2 6.8 (4.5 to 9.1) <.001 
 European total laps 10.7 ± 6.7 15.8 ± 8.2 5.1 (2.8 to 7.4) <.001 
 Global total laps 10.3 ± 7.0 15.4 ± 7.9 5.1 (2.7 to 7.4) <.001 
Girls     
 Total laps 9.1 ± 5.2 12.3 ± 5.8 3.2 (1.5 to 4.9) <.001 
 Z-score total laps 9.5 ± 5.1 12.5 ± 6.2 3.1 (1.3 to 4.8) <.001 
 European total laps 8.1 ± 4.2 12.1 ± 6.0 3.9 (2.2 to 5.7) <.001 
 Global total laps 8.2 ± 4.2 11.7 ± 6.0 3.6 (1.8 to 5.4) <.001 
CRF Median ThresholdGroup 1 VPA (mins)Group 2 VPA (mins)Δ (95% CI)P
Boys     
 Total laps 10.7 ± 6.7 15.9 ± 8.3 5.3 (3.0 to 7.5) <.001 
 Z-score total laps 10.8 ± 6.6 17.6 ± 8.2 6.8 (4.5 to 9.1) <.001 
 European total laps 10.7 ± 6.7 15.8 ± 8.2 5.1 (2.8 to 7.4) <.001 
 Global total laps 10.3 ± 7.0 15.4 ± 7.9 5.1 (2.7 to 7.4) <.001 
Girls     
 Total laps 9.1 ± 5.2 12.3 ± 5.8 3.2 (1.5 to 4.9) <.001 
 Z-score total laps 9.5 ± 5.1 12.5 ± 6.2 3.1 (1.3 to 4.8) <.001 
 European total laps 8.1 ± 4.2 12.1 ± 6.0 3.9 (2.2 to 5.7) <.001 
 Global total laps 8.2 ± 4.2 11.7 ± 6.0 3.6 (1.8 to 5.4) <.001 

Boys and girls were divided into either group 1 (≤50th percentile) or group 2 (>50th percentile) based on the median of the total number of laps completed on the 20m shuttle run test, of z-score total laps, of European normative data for total laps,39  and of global normative data for total laps.40  Data are represented as mean ± standard deviation. Vigorous physical activity was based on the bandpass-filtered followed by Euclidean norm metric. CI, confidence intervals of the mean difference between groups; Δ, mean difference (group 2 – group 1).

Habitual PA is known to have many health benefits, including higher levels of CRF in all age-groups. We showed that greater average VPA per day was associated with better CRF in our adolescent population up to about 20 minutes of VPA per day. The association plateaued after this, with even greater VPA durations not being associated with significant further improvement of CRF. After intercorrelations between PA intensities were controlled for, only VPA was associated with CRF. As current guidelines set PA activity targets using a combined metric of MVPA that could be satisfied by undertaking only MPA and not VPA, we provide grounds for clearer public health messaging on how to improve CRF in this population. Most adolescents fail to achieve the current recommendation of ≥60 minutes of MVPA per day on average.18,19  One possible reason is that this duration is quite long, requiring a daily time commitment that some may find difficult to maintain. A shorter target of 20 minutes might be easier to schedule daily and a focus on VPA would simplify messages about the intensity of activity that is likely to improve CRF.

Our findings support those in adults where 15 minutes of VPA was shown to improve both CRF and glycemic control,16  and are similar to findings in children and adolescents.15  The current guidelines provide recommendations based on a combined metric of MVPA, which does not specify the duration of either MPA or VPA.18  Our approach provides such evidence and highlights the importance of VPA over lesser intensity PA for CRF.

Our findings are consistent with other cross-sectional studies in highlighting the benefits of VPA.15,41  However, to our knowledge, no prior study has attempted to examine the independent effects of different PA intensities, controlling for their intercorrelation. Our approach suggests that, although past reports generally acknowledge the inferiority of MPA or lesser intensities of PA, conclusions about MPA could have been erroneously drawn because of the confounding strong correlation between VPA and MPA.

We showed no additional effect of MPA or lower intensity PA on CRF beyond the influence of VPA. Nevertheless, it is possible that other health-related measures that we did not study may improve with lower intensities of PA.42  However, as we showed, it is important to first control for intercorrelations between PA intensities before examining associations with any health-related measures. Nevertheless, CRF remains an important predictor of cardiometabolic health outcomes.69  It is notable too that earlier studies have generally found that only very high amounts of MPA are associated with improved health outcomes.1  In studies of exercise interventions, VPA interventions were found to have similar adherence rates to those with lesser intensity, but with reduced time commitments, highlighting no apparent disadvantage of focusing on VPA.41  Cross-sectional and longitudinal evidence also suggests that VPA improves adiposity and cardiometabolic health to a greater extent than MPA.15,41,43 

Undertaking 20 minutes of daily VPA to improve CRF may provide the means for adolescents to improve their long-term health outcomes at scale.69,20,21  However, our study design does not address causation in the associations we have shown. Although reverse causation may partially explain our findings, prior knowledge on the relationship between exercise and CRF suggests this is unlikely to be a dominant explanatory factor.14  Future studies should also aim to evaluate how best to achieve a target of 20 minutes of daily VPA in large populations, as recent studies have shown that it can be difficult to achieve similar VPA targets at scale.44 

We found that, on average, girls undertook less PA and had lower CRF than boys, which is in-line with previous studies.19,45  However, we found no evidence that our models of the relationship between PA and CRF differed significantly according to sex, supporting the use of the combined model and unified recommendations for all.

This work has a number of strengths and potential limitations. Our findings are based on data from two Oxfordshire, United Kingdom schools and therefore, might not be generalizable to broader populations. However, the sample was well-balanced by sex, covered a range of socioeconomic and ethnic groups, and had a wide distribution of BMI and CRF. Although we acknowledge that our study needs replicating, importantly, there was no significant difference in shuttle run performance between our data and European and Global normative data, suggesting that our population is representative.39,40  Recently the validity of the 20mSRT as an indirect assessment of CRF has been both questioned46  and defended.47  Although its utility and validity for ranking fitness in large populations of children is supported in numerous contemporary reviews,4851  we acknowledge that replication of our findings with more direct assessment of VO2max would be desirable, although challenging in practice. There is no universally-accepted metric to analyze PA derived from accelerometry.31  We took care in our data processing to calibrate our accelerometery and ensure accurate, manually curated, identification of wear periods. We also used BFEN in our analyses, as this was shown to classify VPA better than other metrics in our target age-group.33  However, we repeated our analyses using the variety of other PA metrics favored in the literature, which did not meaningfully alter our results.

We provide evidence that adolescents who undertake 20 minutes of daily VPA on average have maximal CRF, with little evidence of additional benefit from undertaking more or from lesser intensities of PA. This modest duration and more specific intensity of PA may provide a better underpinning for future guidelines that currently recommend a longer duration of less specific activity (MVPA) each day. Further work should aim to test whether interventions based on this new target offer significant improvements in adolescent cardiometabolic health.

Mr Burden and Dr Jones conceptualized and designed the study, designed data analyses tools, and drafted the initial manuscript; Drs Weedon and Meaney, Mr Dawes, Mr Whaymand, and Ms Turner coordinated and supervised data collection, collected data, and conducted the initial analyses; and all authors reviewed and revised the manuscript and approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2022-057026.

FUNDING: Dr Jones is supported by a British Heart Foundation, Intermediate Clinical Research Fellowship (FS/18/22/33479); Professor Dawes is supported by the Elizabeth Casson Trust and the U.K. National Institute for Health Research Oxford Health Biomedical Research Centre; Mr Burden is supported by the Professor Nigel Groome Studentship scheme (Oxford Brookes University); and Dr Weedon is supported by the British Heart Foundation grant held by Dr Jones. The views expressed are those of the authors and not necessarily those of the British Heart Foundation, the U.K. National Institute of Health Research, or the U.K. Department of Health. The other authors received no additional funding.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no financial relationships relevant to this article to disclose.

     
  • AIC

    Akaike information criterion

  •  
  • BFEN

    bandpass-filtered followed by Euclidean norm

  •  
  • BMI

    body mass index

  •  
  • CRF

    cardiorespiratory fitness

  •  
  • CVD

    cardiovascular disease

  •  
  • ENMO

    Euclidean norm minus 1

  •  
  • ENMOz –

    Euclidean norm minus 1 with negative values rounded to 0

  •  
  • HFEN

    highpass filtered Euclidean norm

  •  
  • HFEN+

    highpass filtered Euclidean norm plus

  •  
  • LPA

    light physical activity

  •  
  • MPA

    moderate physical activity

  •  
  • MVPA

    moderate to vigorous physical activity

  •  
  • PA

    physical activity

  •  
  • rPA

    residualised physical activity after partial multivariable regression modelling

  •  
  • ST

    sedentary time

  •  
  • VPA

    vigorous physical activity

  •  
  • WHO

    World Health Organization

  •  
  • 20mSRT

    20 meter shuttle run test

1
Ramakrishnan
R
,
Doherty
A
,
Smith-Byrne
K
, et al
.
Accelerometer measured physical activity and the incidence of cardiovascular disease: evidence from the UK Biobank cohort study
.
PLoS Med
.
2021
;
18
(
1
):
e1003487
2
Mandsager
K
,
Harb
S
,
Cremer
P
,
Phelan
D
,
Nissen
SE
,
Jaber
W
.
Association of cardiorespiratory fitness with long-term mortality among adults undergoing exercise treadmill testing
.
JAMA Netw Open
.
2018
;
1
(
6
):
e183605
3
McKinney
J
,
Lithwick
DJ
,
Morrison
BN
, et al
.
The health benefits of physical activity and cardiorespiratory fitness
.
B C Med J
.
2016
;
58
(
3
):
131
137
4
Hemmingsson
E
,
Väisänen
D
,
Andersson
G
,
Wallin
P
,
Ekblom-Bak
E
.
Combinations of BMI and cardiorespiratory fitness categories: trends between 1995 and 2020 and associations with CVD incidence and mortality and all-cause mortality in 471 216 adults
.
Eur J Prev Cardiol
.
2021
;
zwab169
5
Biddle
SJ
,
Asare
M
.
Physical activity and mental health in children and adolescents: a review of reviews
.
Br J Sports Med
.
2011
;
45
(
11
):
886
895
6
Schmidt
MD
,
Magnussen
CG
,
Rees
E
,
Dwyer
T
,
Venn
AJ
.
Childhood fitness reduces the long-term cardiometabolic risks associated with childhood obesity
.
Int J Obes
.
2016
;
40
(
7
):
1134
1140
7
Mikkelsson
L
,
Kaprio
J
,
Kautiainen
H
,
Nupponen
H
,
Tikkanen
MJ
,
Kujala
UM
.
Endurance running ability at adolescence as a predictor of blood pressure levels and hypertension in men: a 25-year follow-up study
.
Int J Sports Med
.
2005
;
26
(
6
):
448
452
8
Camhi
SM
,
Katzmarzyk
PT
,
Broyles
S
, et al
.
Association of metabolic risk with longitudinal physical activity and fitness: coronary artery risk development in young adults (CARDIA)
.
Metab Syndr Relat Disord
.
2013
;
11
(
3
):
195
204
9
Carnethon
MR
,
Gidding
SS
,
Nehgme
R
,
Sidney
S
,
Jacobs
DR
Jr
,
Liu
K
.
Cardiorespiratory fitness in young adulthood and the development of cardiovascular disease risk factors
.
JAMA
.
2003
;
290
(
23
):
3092
3100
10
Ekelund
U
,
Anderssen
SA
,
Froberg
K
,
Sardinha
LB
,
Andersen
LB
,
Brage
S
;
European Youth Heart Study Group
.
Independent associations of physical activity and cardiorespiratory fitness with metabolic risk factors in children: the European youth heart study
.
Diabetologia
.
2007
;
50
(
9
):
1832
1840
11
DeFina
LF
,
Haskell
WL
,
Willis
BL
, et al
.
Physical activity versus cardiorespiratory fitness: two (partly) distinct components of cardiovascular health?
Prog Cardiovasc Dis
.
2015
;
57
(
4
):
324
329
12
Bye
A
,
Klevjer
M
,
Ryeng
E
, et al
.
Identification of novel genetic variants associated with cardiorespiratory fitness
.
Prog Cardiovasc Dis
.
2020
;
63
(
3
):
341
349
13
Timmons
JA
,
Knudsen
S
,
Rankinen
T
, et al
.
Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans
.
J Appl Physiol (1985)
.
2010
;
108
(
6
):
1487
1496
14
Dobbins
M
,
Husson
H
,
DeCorby
K
,
LaRocca
RL
.
School-based physical activity programs for promoting physical activity and fitness in children and adolescents aged 6 to 18
.
Cochrane Database Syst Rev
.
2013
;
2013
(
2
):
CD007651
15
Gralla
MH
,
McDonald
SM
,
Breneman
C
,
Beets
MW
,
Moore
JB
.
Associations of objectively measured vigorous physical activity with body composition, cardiorespiratory fitness, and cardiometabolic health in youth: a review
.
Am J Lifestyle Med
.
2016
;
13
(
1
):
61
97
16
Gibala
MJ
,
Little
JP
.
Physiological basis of brief vigorous exercise to improve health
.
J Physiol
.
2020
;
598
(
1
):
61
69
17
Raghuveer
G
,
Hartz
J
,
Lubans
DR
, et al;
American Heart Association Young Hearts Athero, Hypertension and Obesity in the Young Committee of the Council on Lifelong Congenital Heart Disease and Heart Health in the Young
.
Cardiorespiratory fitness in youth: an important marker of health: a scientific statement from the American Heart Association
.
Circulation
.
2020
;
142
(
7
):
e101
e118
18
Bull
FC
,
Al-Ansari
SS
,
Biddle
S
, et al
.
World Health Organization 2020 guidelines on physical activity and sedentary behaviour
.
Br J Sports Med
.
2020
;
54
(
24
):
1451
1462
19
Guthold
R
,
Stevens
GA
,
Riley
LM
,
Bull
FC
.
Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants
.
Lancet Child Adolesc Health
.
2020
;
4
(
1
):
23
35
20
Sandercock
GR
,
Ogunleye
A
,
Voss
C
.
Six-year changes in body mass index and cardiorespiratory fitness of English schoolchildren from an affluent area
.
Int J Obes (Lond)
.
2015
;
39
(
10
):
1504
1507
21
Sandercock
G
,
Voss
C
,
McConnell
D
,
Rayner
P
.
Ten year secular declines in the cardiorespiratory fitness of affluent English children are largely independent of changes in body mass index
.
Arch Dis Child
.
2010
;
95
(
1
):
46
47
22
Weedon
BD
,
Liu
F
,
Mahmoud
W
, et al
.
Declining fitness and physical education lessons in UK adolescents
.
BMJ Open Sport Exerc Med
.
2022
;
8
(
1
):
e001165
23
von Elm
E
,
Altman
DG
,
Egger
M
,
Pocock
SJ
,
Gøtzsche
PC
,
Vandenbroucke
JP
;
STROBE Initiative
.
Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies
.
BMJ
.
2007
;
335
(
7624
):
806
808
24
de Onis
M
,
Onyango
AW
,
Borghi
E
,
Siyam
A
,
Nishida
C
,
Siekmann
J
.
Development of a WHO growth reference for school-aged children and adolescents
.
Bull World Health Organ
.
2007
;
85
(
9
):
660
667
25
Ramsbottom
R
,
Brewer
J
,
Williams
C
.
A progressive shuttle run test to estimate maximal oxygen uptake
.
Br J Sports Med
.
1988
;
22
(
4
):
141
144
26
Ekblom
O
,
Nyberg
G
,
Bak
EE
,
Ekelund
U
,
Marcus
C
.
Validity and comparability of a wrist-worn accelerometer in children
.
J Phys Act Health
.
2012
;
9
(
3
):
389
393
27
Hamid
A
,
Duncan
MJ
,
Eyre
ELJ
,
Jing
Y
.
Predicting children’s energy expenditure during physical activity using deep learning and wearable sensor data
.
Eur J Sport Sci
.
2021
;
21
(
6
):
918
926
28
Hedayatrad
L
,
Stewart
T
,
Duncan
S
.
Concurrent validity of ActiGraph GT3X+ and Axivity AX3 accelerometers for estimating physical activity and sedentary behavior
.
J Meas Phys Behav
.
2021
;
4
(
1
):
1
8
29
Narayanan
A
,
Stewart
T
,
Mackay
L
.
A dual-accelerometer system for detecting human movement in a free-living environment
.
Med Sci Sports Exerc
.
2020
;
52
(
1
):
252
258
30
Petersen
TL
,
Brønd
JC
,
Kristensen
PL
,
Aadland
E
,
Grøntved
A
,
Jepsen
R
.
Resemblance in accelerometer-assessed physical activity in families with children: the Lolland-Falster health study
.
Int J Behav Nutr Phys Act
.
2020
;
17
(
1
):
161
31
van Hees
VT
,
Gorzelniak
L
,
Dean León
EC
, et al
.
Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity
.
PLoS One
.
2013
;
8
(
4
):
e61691
32
Rowlands
AV
,
Olds
TS
,
Hillsdon
M
, et al
.
Assessing sedentary behavior with the GENEActiv: introducing the sedentary sphere
.
Med Sci Sports Exerc
.
2014
;
46
(
6
):
1235
1247
33
VAN Loo
CMT
,
Okely
AD
,
Batterham
MJ
, et al
.
Wrist acceleration cut points for moderate-to-vigorous physical activity in youth
.
Med Sci Sports Exerc
.
2018
;
50
(
3
):
609
616
34
Schaefer
CA
,
Nigg
CR
,
Hill
JO
,
Brink
LA
,
Browning
RC
.
Establishing and evaluating wrist cutpoints for the GENEActiv accelerometer in youth
.
Med Sci Sports Exerc
.
2014
;
46
(
4
):
826
833
35
Hildebrand
M
,
Hansen
BH
,
van Hees
VT
,
Ekelund
U
.
Evaluation of raw acceleration sedentary thresholds in children and adults
.
Scand J Med Sci Sports
.
2017
;
27
(
12
):
1814
1823
36
Hildebrand
M
,
VAN Hees
VT
,
Hansen
BH
,
Ekelund
U
.
Age group comparability of raw accelerometer output from wrist- and hip-worn monitors
.
Med Sci Sports Exerc
.
2014
;
46
(
9
):
1816
1824
37
Brønd
JC
,
Andersen
LB
,
Arvidsson
D
.
Generating ActiGraph counts from raw acceleration recorded by an alternative monitor
.
Med Sci Sports Exerc
.
2017
;
49
(
11
):
2351
2360
38
Allen
MP
.
Partial regression and residualized variables
. In:
Understanding Regression Analysis
, 1st ed.
Boston, MA
:
Springer US
;
1997
:
86
90
39
Tomkinson
GR
,
Carver
KD
,
Atkinson
F
, et al
.
European normative values for physical fitness in children and adolescents aged 9-17 years: results from 2 779 165 Eurofit performances representing 30 countries
.
Br J Sports Med
.
2018
;
52
(
22
):
1445
14563
40
Tomkinson
GR
,
Lang
JJ
,
Tremblay
MS
, et al
.
International normative 20 m shuttle run values from 1 142 026 children and youth representing 50 countries
.
Br J Sports Med
.
2017
;
51
(
21
):
1545
1554
41
Owens
S
,
Galloway
R
,
Gutin
B
.
The case for vigorous physical activity in youth
.
Am J Lifestyle Med
.
2016
;
11
(
2
):
96
115
42
Poitras
VJ
,
Gray
CE
,
Borghese
MM
, et al
.
Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth
.
Appl Physiol Nutr Metab
.
2016
;
41
(
6 Suppl 3
):
S197
S239
43
Carson
V
,
Rinaldi
RL
,
Torrance
B
, et al
.
Vigorous physical activity and longitudinal associations with cardiometabolic risk factors in youth
.
Int J Obes (Lond)
.
2014
;
38
(
1
):
16
21
44
Wassenaar
TM
,
Wheatley
CM
,
Beale
N
, et al
.
The effect of a one-year vigorous physical activity intervention on fitness, cognitive performance and mental health in young adolescents: the Fit to Study cluster randomised controlled trial
.
Int J Behav Nutr Phys Act
.
2021
;
18
(
1
):
47
45
Armstrong
N
,
Welsman
J
.
Youth cardiorespiratory fitness: evidence, myths and misconceptions
.
Bull World Health Organ
.
2019
;
97
(
11
):
777
782
46
Welsman
J
,
Armstrong
N
.
The 20 m shuttle run is not a valid test of cardiorespiratory fitness in boys aged 11-14 years
.
BMJ Open Sport Exerc Med
.
2019
;
5
(
1
):
e000627
47
Nevill
AM
,
Ramsbottom
R
,
Sandercock
G
,
Bocachica-González
CE
,
Ramírez-Vélez
R
,
Tomkinson
G
.
Response to the comment by Armstrong and Welsman on ‘developing a new curvilinear allometric model to improve the fit and validity of the 20-m shuttle run test as a predictor of cardiorespiratory ritness in adults and youth’
.
Sports Med
.
2021
;
51
(
7
):
1595
1597
48
Castro-Piñero
J
,
Artero
EG
,
España-Romero
V
, et al
.
Criterion-related validity of field-based fitness tests in youth: a systematic review
.
Br J Sports Med
.
2010
;
44
(
13
):
934
943
49
Mayorga-Vega
D
,
Aguilar-Soto
P
,
Viciana
J
.
Criterion-related validity of the 20-M shuttle run test for estimating cardiorespiratory fitness: a meta-analysis
.
J Sports Sci Med
.
2015
;
14
(
3
):
536
547
50
Mayorga-Vega
D
,
Bocanegra-Parrilla
R
,
Ornelas
M
,
Viciana
J
.
Criterion-related validity of the distance- and time-based walk/run field tests for estimating cardiorespiratory fitness: a systematic review and meta-analysis
.
PLoS One
.
2016
;
11
(
3
):
e0151671
51
Ruiz
JR
,
Silva
G
,
Oliveira
N
,
Ribeiro
JC
,
Oliveira
JF
,
Mota
J
.
Criterion-related validity of the 20-m shuttle run test in youths aged 13-19 years
.
J Sports Sci
.
2009
;
27
(
9
):
899
906

Supplementary data