Cardiorespiratory fitness is defined as the maximal ability of the cardiorespiratory system to transport and consume oxygen (1). Higher levels of cardiorespiratory fitness during childhood are associated with lower risk of cardiovascular disease (2,3) and myocardial infarction (4) in adulthood. Therefore, accurate, appropriate, and clinically useful quantification of cardiorespiratory fitness is essential for identifying low levels of cardiorespiratory fitness in children.
Cardiorespiratory fitness is typically assessed by measuring maximal oxygen uptake (V̇O2max) during a maximal incremental or ramp exercise test. V̇O2max is most commonly quantified and reported relative to body mass (BM; mL·kg−1⋅min−1). However, in individuals with obesity, a strong inverse association exists between V̇O2max relative to BM (i.e., mL·kg−1⋅min−1) and BM, which is independent of true aerobic capacity as shown by Buskirk and Taylor (5). Also, it is well recognized that obesity or excess adipose tissue per se does not affect the ability of the cardiorespiratory and muscular systems to deliver and use oxygen in adults (5) or children (6). Nevertheless, there is a preponderance of literature in children with obesity that quantifies V̇O2max relative to BM (i.e., mL·kg−1⋅min−1), perpetuating the misconception that all children with obesity are deconditioned or have low cardiorespiratory fitness (7).
To circumvent the BM confounding problem in children with obesity, V̇O2max could be quantified relative to lean body mass (LBM) (8). V̇O2max prediction equations based on LBM are available for children ages 8–18 yr (9). However, measurements of LBM are rare in clinical practice, and thus these equations may have limited clinical utility in children with obesity.
Quantifying V̇O2max based on “ideal” BM has been recommended as standard of care for all adults with obesity (10). A two-step approach to quantify V̇O2max based on “ideal” BM was first suggested by Hansen et al. (11) in 1984 to limit the influence of fat mass on the quantification of cardiorespiratory fitness. The authors proposed first predicting “ideal” BM based on the individual’s height and then predicting V̇O2max based on age, height, and “ideal” BM in men and women (10–12). A similar approach has been used by Blanchard et al. (13) in 12- to 18-yr-old adolescents by first predicting BM and then using predicted BM to estimate percent predicted V̇O2max in a multivariate model. Because height and weight changes in children with age, predicted BM must be established using sex- and age-based body mass index (BMI) percentiles. Blanchard et al. (13) suggested predicting BM at the 85th BMI percentile (i.e., lower limit of overweight) for any adolescent with a BMI above the 85th percentile and using this as “corrected” BM in a multivariate V̇O2max prediction model. However, it remains unclear whether predicted V̇O2max based on predicted BM at the 85th BMI percentile is influenced by fat mass in children when it is used alone instead of in a multivariate model because multivariate models are not currently available for children below 12 yr. It is also unclear whether predicted V̇O2max based on predicted BM indexed to the median value of the central distribution of BMI (i.e., 50th BMI percentile) is less influenced by fat mass in children. No previous studies have used predicted BM at the 50th or 85th BMI percentile for estimating percent predicted V̇O2max in children with obesity, an approach that would be comparable to standard of care “ideal” BM scaling in adults.
The purpose of this study was to identify methods of quantifying cardiorespiratory fitness that were less influenced by fat mass in children with obesity. We compared different approaches for quantifying cardiorespiratory fitness, including absolute (i.e., L·min−1) and relative (i.e., mL·kg−1⋅min−1 BM, mL·kg−1⋅min−1, LBM, and % predicted) methods. We hypothesized that predicted V̇O2max measurements based on LBM and predicted BM would be less influenced by fat mass and would better identify exercise-impaired children with obesity than cardiorespiratory fitness measurements based on measured BM.
These data were collected as part of a larger project examining the respiratory effects of obesity in children, and some of these data have been published elsewhere (14). Furthermore, some of the results of these studies have been previously reported in the form of an abstract (15).
All study procedures were explained in detail to the children and parents/guardians. Parents or guardians provided written, informed consent, and children provided written assent before any study procedures. The UT Southwestern Institutional Review Board approved this study (no. 052012–076). Data from tests in 53 children, 27 with obesity (BMI > 95th percentile for age and sex; range: 95th BMI percentile—152% above the 95th BMI percentile) and 26 without obesity (BMI 5th to 84th percentile for age and sex; range: 14–83), were included. All children were 8–<13 yr old (range: 8.7–12.8 yr) when they were recruited for the larger project.
Participants completed a physical activity readiness questionnaire, which is a part of our standard screening process (16); only participants that answered “no” to all questions were allowed to participate. Participants also completed a self-report Tanner pubertal stage assessment (17) and a Physical Activity Questionnaire for Children (18). Height was recorded using an eye-level physical scale, and weight was recorded using a calibrated weighing scale. BMI percentile was calculated for each child using age- and sex-specific BMI tables from the Centers for Disease Control and Prevention (CDC) (19).
Dual-energy x-ray absorptiometry
Participants underwent a dual-energy x-ray absorptiometry (DXA) scan for assessment of body composition (Lunar Prodigy Advance; GE Healthcare Lunar, Madison, WI). Whole-body fat mass and LBM were estimated using Prodigy enCore software (GE Healthcare Lunar). DXA offers valid (20) and reproducible (21) estimates of body composition in children.
Maximal exercise test and supramaximal verification test
The procedures for incremental and verification testing have been described in detail previously (14). Briefly, the incremental exercise test was performed on a cycle ergometer (Lode Corival, The Netherlands) to the point of volitional exhaustion. The initial work rate was 20 W, and the work rate was increased by 15 W (n = 7) or 10 W (n = 46) increments every minute, with no differences between the two increments for eliciting V̇O2max as described previously (14). After 15 min of seated rest, participants performed a constant-load supramaximal verification test at 105% of the maximal work rate. Expired gases were collected in polyurethane bags for the determination of minute ventilation, V̇O2, and expired carbon dioxide. The highest V̇O2 from the incremental or verification test was used as V̇O2max for the participant.
Blanchard et al. (13) suggested the use of predicted BM at the 85th BMI percentile as “corrected” BM for overweight adolescents ages 12–17 yr. The current article tested a second approach of predicting BM at the 50th BMI percentile and comparing it against predicted BM at the 85th percentile with respect to the prediction of cardiorespiratory fitness from BM-based prediction equations. BMI-for-age data from the CDC (19) were used to calculate “predicted” BM (i.e., BM at the 50th and 85th BMI percentile) for children with obesity. Predicted BM at the 50th percentile was also calculated for children without obesity who were above the 50th BMI percentile (n = 17). Predicted BM at the 50th percentile was 25.3 ± 11.9 kg lower than BM in children with obesity and 3.3 ± 2.4 kg lower than BM in children without obesity (n = 17). Predicted BM at the 85th percentile was 18.5 ± 10.9 kg lower than BM in children with obesity.
BM, predicted BM at the 50th and 85th BMI percentiles, LBM, and age were used to calculate predicted V̇O2max using the sex-specific prediction equations given below (9,22,23). The equations of Cooper et al. (9,22) were derived from cycle ergometer testing in children in the United States. The age-based equations of Armstrong et al. (24) were derived from pooled data from cycle ergometer testing in 3050 male and 2167 female participants.
- Cooper et al. (22): (0.0528 × BM) − 0.303.
- Cooper et al. (9): (0.052 × BM) − 0.266.
Cooper et al. (9): (0.059 × LBM) − 0.103.
Armstrong et al. (24): (0.257 × age) − 1.107.
- Cooper et al. (22): (0.0285 × BM) + 0.288.
- Cooper et al. (9): (0.037 × BM) + 0.022.
Cooper et al. (9): (0.055 × LBM) − 0.187.
Armstrong et al. (24): (0.131 × age) + 0.123.
Percent predicted V̇O2max was calculated as measured V̇O2max / predicted V̇O2max × 100. For percent predicted, ≥100% was considered normal fitness, whereas 75%–99%, 50%–74%, and ≤50% reflected degrees of functional impairment (10).
Data were expressed as mean and SD values. A two-tailed distribution was used to test for statistical significance (P < 0.050). Independent t-tests (for normally distributed data) or Mann–Whitney U tests (for data that were not normally distributed) were used to compare differences by group (with vs without obesity). For linear associations, Pearson correlations and linear regressions were used to examine associations between variables. For nonlinear associations, Spearman correlation analyses were performed. Two-way repeated-measures ANOVA was used to compare differences between incremental and verification V̇O2 between children with and without obesity. Two-way repeated-measures ANOVA was used to compare differences between measured V̇O2max and predicted V̇O2max based on LBM between children with and without obesity. Chi-square test was used to estimate frequency differences in sex, Tanner pubertal stage, and fitness categories between children with and without obesity. These analyses were performed using SPSS 24 (IBM, Armonk, NY). Differences and interactions in regression slopes between children with and without obesity were tested using SAS software (SAS Institute Inc., Cary, NC).
Participant characteristics are included in Table 1. DXA scans were not performed for four children with obesity; thus, fat mass and LBM data were not available for these children. Differences in age, height, Tanner stage, or Physical Activity Questionnaire for Children scores between children with and without obesity were not statistically significant (Table 1). As expected, children with obesity had higher BM, BMI, percent body fat, and fat mass compared with children without obesity (Table 1). Children with obesity also had 7 kg more of LBM compared with children with obesity (P = 0.004; Table 1).
Peak V̇O2 from the verification test (1.59 ± 0.39 L·min−1) was significantly higher than peak V̇O2 from the incremental test (1.48 ± 0.35 L·min−1) with no significant test–group interaction. Peak HR values from the incremental and verification tests were higher in children without obesity (191 ± 10 and 189 ± 10 bpm, respectively) compared with children with obesity (184 ± 11 and 182 ± 11 bpm, respectively; P (group) = 0.007, P (test) = 0.055, P (interaction) = 0.342). Peak respiratory exchange ratios from the incremental and verification tests were higher in children without obesity (1.14 ± 0.07 and 1.10 ± 0.10 bpm, respectively) compared with children with obesity (1.08 ± 0.06 and 1.04 ± 0.13 bpm, respectively; P (group) = 0.016, P (test) = 0.024, P (interaction) = 0.785).
Methods for quantifying cardiorespiratory fitness
Cardiorespiratory fitness quantified using different methods is presented for children with and without obesity in Table 2. When compared with children without obesity, children with obesity showed significantly lower levels of cardiorespiratory fitness when V̇O2max was quantified relative to BM (mL·kg−1 BM⋅min−1) and LBM (mL·kg−1 LBM⋅min−1), as well as percent predicted V̇O2max based on BM (9,22), predicted BM at the 85th percentile (9,22), and age (23) (Table 2). By contrast, differences in cardiorespiratory fitness between children with and without obesity for absolute V̇O2max (L·min−1) or percent predicted V̇O2max based on predicted BM at the 50th percentile were not statistically significant (9,22) (Table 2).
Relationships with fat mass and LBM
BM explained 25% of the variance in V̇O2max (L·min−1), whereas LBM explained 55% of the variance in V̇O2max in children without obesity (Figs. 1A and 1B). In children with obesity, BM explained 80% of the variance in V̇O2max, (L·min−1), whereas LBM explained 79% of the variance in V̇O2max. Differences in the regression slopes for V̇O2max versus BM and LBM between children with and without obesity were not statistically significant (P = 0.84 and 0.76, respectively; Figs. 1A and 1B). Figures 1C to 1F demonstrate that predicted V̇O2max based on BM would grossly underestimate cardiorespiratory fitness in boys and girls with obesity, whereas V̇O2max based on LBM would offer relatively accurate estimates in boys and girls with obesity. Direct sex comparisons have not been conducted because there were only seven girls with obesity in this study.
V̇O2max relative to BM (mL·kg−1⋅min−1) was strongly and inversely associated with fat mass (r = −0.857, P < 0.001), while being weakly and inversely associated with LBM (r = − 0.341; P = 0.017). Predicted V̇O2max based on LBM and predicted BM at the 50th BMI percentile were strongly associated with LBM, while being moderately or weakly associated with fat mass (Fig. 2). By contrast, predicted V̇O2max based on BM and predicted BM at the 85th BMI percentile were moderately or strongly associated with fat mass (Fig. 2).
Predicting V̇O2max based on LBM
Measured V̇O2max and predicted V̇O2max based on LBM were strongly correlated in children with and without obesity, with no difference in the slopes of the regression lines (P = 0.76; Fig. 3). Measured V̇O2max was lower than predicted V̇O2max based on LBM in children with obesity (P < 0.001) but not in children without obesity (P = 0.45).
Identifying deconditioned children
Normative data were available for absolute and relative to BM V̇O2max in children (25). Comparing participant V̇O2max (L·min−1) to the absolute V̇O2max normative data, 93% of children with obesity had “fair” to “excellent” cardiorespiratory fitness and 7% of children with obesity had poor cardiorespiratory fitness (Fig. 4A). By contrast, when using the relative to BM V̇O2max normative data, 100% of children with obesity were found to have “poor” or “very poor” cardiorespiratory fitness (Fig. 4B). On the basis of percent predicted V̇O2max from LBM, 17% of children with obesity were ≥100% predicted or classified as having normal cardiorespiratory fitness (Fig. 4C). On the basis of percent predicted V̇O2max from predicted BM at the 85th BMI percentile, 11% of children with obesity were classified as having normal cardiorespiratory fitness. On the basis of percent predicted V̇O2max from predicted BM at the 50th BMI percentile, 30% of children with obesity were classified as having normal cardiorespiratory fitness (Fig. 4D). The percent predicted LBM and predicted BM methods did not identify any children with obesity with severe functional impairment (i.e., V̇O2max ≤ 50% predicted).
To the best of our knowledge, this was the first article to identify methods of quantifying cardiorespiratory fitness that were least influenced by fat mass in children with obesity. Our findings suggested that V̇O2max quantified relative to BM (i.e., mL·kg−1⋅min−1) was significantly influenced by fat mass and, thus, may not be suitable for quantifying cardiorespiratory fitness in children with obesity. We also showed that percent predicted V̇O2max based on LBM or predicted BM at the 50th BMI percentile methods were less influenced by fat mass and, thus, could be more suitable for quantifying cardiorespiratory fitness in children with obesity. A percent predicted V̇O2max estimation based on predicted BM is already standard of care for adults with obesity (10) and arguably should become standard of care for children with obesity. Percent predicted V̇O2max based on LBM or predicted BM at the 50th BMI percentile may be used to identify deconditioned children with obesity and help direct healthcare resources and treatment strategies appropriately. These methods of quantifying cardiorespiratory fitness that are less influenced by fat mass also allow for meaningful comparisons between children with and without obesity. Finally, clinicians may consider measuring body composition (i.e., LBM and fat mass) for children with obesity undergoing cardiopulmonary exercise testing as it may assist with the interpretation of cardiorespiratory fitness.
The strong association between V̇O2max quantified relative to BM (i.e., mL·kg−1⋅min−1) and BM was first shown by Buskirk (26) in healthy adults and has since been shown in elite athletes ranging in BW from 55 to 95 kg (27) and in children ranging in BM from 142% to 205% predicted based on height (6). This strong association between V̇O2max quantified relative to BM (i.e., mL·kg−1⋅min−1) and BM limits the ability to perform meaningful comparisons between children with and without obesity. The point becomes unambiguously apparent when looking at Figure 1C. A typical 75-kg boy with obesity would be expected to reach a V̇O2max of 3.5 L·min−1 to be considered “normal” based on his BM. This estimate is partly based on the assumption that a typical 75-kg boy with obesity carries the same amount of LBM as a typical 75-kg boy without obesity. Assuming that boys without obesity have 22% body fat on average, this 75-kg boy with obesity would be expected to have 59 kg of LBM if he had 22% body fat. However, upon measurement, the body with obesity had 53% body fat (i.e., 40 kg of LBM), which meant that his LBM was 19 kg lower than the expected value of 59 kg and that the extra 19 kg was essentially metabolically inactive fat mass. A boy carrying 40 kg of LBM would be expected to achieve a V̇O2max of 2.3 L·min−1 based on LBM (9), which would be much closer to measured values of V̇O2max, unless the boy was exercise impaired or deconditioned.
It may also be incorrect to compare measured V̇O2max quantified relative to BM from a child with obesity to a normative data set (25) because all children with obesity would be classified as deconditioned as seen in Figure 4B, even if the ability of their cardiorespiratory system to deliver oxygen and their muscular system to use oxygen may not be compromised. This point is clearly apparent when reviewing the literature for V̇O2max cutoffs that help identify children at risk of poor health (28,29). Lang et al. (28) reviewed 10 studies with over 1 million children ages 9–17 yr, and Ruiz et al. (29) reviewed 7 studies with 9280 children ages 8–19 yr. Both reports (28,29) listed cutoffs of approximately 42 mL·kg−1 BM⋅min−1 for boys and 35 mL·kg−1 BM⋅min−1 for girls and recommended raising a clinical “red flag” when children were found to be below these cutoffs. All children with obesity in the present study would have raised a clinical “red flag” based on these cutoffs regardless of their actual risk of poor health, which further provides support that V̇O2max scaled to BM underestimates cardiorespiratory fitness in children with obesity (8,30,31). Inaccurate clinical “red flags” raised for all children with obesity could lead to unnecessary testing, increased healthcare costs, and inappropriate exercise prescriptions. Methods of quantifying cardiorespiratory fitness based on LBM or predicted BM at the 50th BMI percentile are less influenced by fat mass and may better represent cardiorespiratory fitness levels in children with obesity.
Cooper et al. (9) developed sex-based prediction equations for V̇O2max based on BM and LBM in 8- to 18-yr-old children and adolescents with and without obesity. The authors showed that when these equations were applied to otherwise healthy children with obesity, V̇O2max based on BM ranged from 39% to 125% predicted (average ≈62% predicted). In the present article, average V̇O2max based on BM was 60% predicted for children with obesity, which was similar to findings by Cooper et al. (9). However, Cooper et al. (9) and others (13,32) recognize that quantifying V̇O2max relative to BM in children with obesity confounds the measurement of cardiorespiratory fitness because body fat does not contribute significantly to oxygen uptake during exercise. When we substituted predicted BM at the 85th or 50th percentile for BM in the prediction equations, the average cardiorespiratory fitness for children with obesity increased to 84% and 101% predicted, respectively. The estimates of cardiorespiratory fitness based on predicted BM at the 85th percentile were not different from estimates of cardiorespiratory fitness based on LBM.
“Ideal” BM prediction equations are readily available and considered standard of care for quantifying cardiorespiratory fitness in adults with obesity (10,32). Because growth in children affects height and BM simultaneously, it is difficult to apply a standard prediction equation for BM based on height or age in boys and girls. A reasonable alternative involves using normative data from CDC BMI percentiles to predict an “ideal” or “corrected” BM. Whether predicted BM for a child with obesity should be calculated at the 50th or the 85th percentile is an unsettled issue. Previously, Blanchard et al. (13) proposed using the 85th percentile (i.e., the upper limit of normal weight) as “corrected” BM in adolescents as part of a multivariate prediction model for V̇O2max in children ages 8–18 yr. Our data showed that predicted V̇O2max based on predicted BM at the 85th percentile was more influenced by fat mass when compared with predicted V̇O2max based on predicted BM at the 50th percentile. Our data also showed that percent predicted V̇O2max based on LBM did not differ from percent predicted V̇O2max based on predicted BM at the 85th percentile. It is essential that larger studies explore and confirm which approach for predicting “ideal” or “corrected” BM (i.e., 50th vs 85th BMI percentile) may be more appropriate in children with obesity before a consensus can be reached on this important question.
These important findings in a relatively limited number of participants may justify the use of body composition measurements and maximal exercise testing with verification for quantification and interpretation of cardiorespiratory fitness in future larger studies. In addition, although we had a limited number of participants to model in our analysis, our findings were consistent within children with obesity (i.e., V̇O2max quantified relative to BM would lead to a diagnosis of “very poor” cardiorespiratory fitness for all children with obesity). Our data in children with obesity were also consistent with published data in a larger sample of adults (32). As such, our findings support quantifying cardiorespiratory fitness as percent predicted V̇O2max based on LBM or predicted BM at the 50th BMI percentile because these methods were less influenced by excess fat mass. If LBM measurements are not feasible in certain settings, the cardiorespiratory fitness based on predicted BM at the 50th percentile may be acceptable for children with obesity. This is important information for researchers and clinicians who routinely use predicted values based on BM for determining exercise workloads (i.e., submaximal or increment workloads) for cardiopulmonary exercise testing in children with obesity. A better approach would involve using predicted values based on LBM or predicted BM at the 50th BMI percentile for children with obesity.
Strengths and limitations
A strength of the study was that verification testing was used to elicit the V̇O2max in all children (14). Verification testing is recommended as a standard procedure in all populations for confirming achievement of V̇O2max (33). Performing a verification test ensures that the highest level of maximal oxygen uptake (i.e., V̇O2max) was achieved for each individual, thus reducing the need to use the term “V̇O2peak,” which is commonly used when there is uncertainty regarding the achievement of V̇O2max from a single maximal exercise test (33).
Limitations of this study lie in the fact that we had only seven girls with obesity, which reduced our ability to explore sex differences. Future studies using larger sample sizes could identify potential sex differences in different methods of quantifying cardiorespiratory fitness, if any. The cutoff values for percent predicted V̇O2max were derived from a position statement from the American Heart Association in adults (10). There is a need to develop pediatric cutoff values for V̇O2max that are independent of BM and are also predictive of cardiometabolic disease risk. Children with obesity carry more LBM when compared with children without obesity, and there is a strong correlation between LBM and fat mass in children with obesity (r = 0.903). Therefore, using predicted BM at the 50th or 85th BMI percentile would not completely account for the increasing LBM with increasing fat mass in children with obesity. This remains a limitation of using “predicted” or “ideal” BM for quantifying cardiorespiratory fitness based on BM regression equations. New multivariate regression models that are based on variables other than BM (i.e., height, sex, and age) in prepubertal children could overcome this limitation for patients when measurements of LBM are not readily available. Finally, it remains unclear whether results would follow the same pattern in children with morbid obesity who were not included in the present article.
The goal of this article was to identify methods of quantifying cardiorespiratory fitness in children with obesity that were less influenced by fat mass. We showed that quantifying cardiorespiratory fitness based on LBM and predicted BM at the 50th BMI percentile offered estimations that were less influenced by fat mass when compared with quantifying cardiorespiratory fitness based on measured BM. Therefore, quantifying cardiorespiratory fitness based on LBM or predicted BM at the 50th BMI percentile should be preferred over measured BM to assess cardiorespiratory fitness in children with obesity. These methods may be used in the future to help identify exercise-impaired or deconditioned children with obesity who may require treatment for cardiorespiratory or other illnesses, which is an important national health concern.
The authors thank Benjamin Levine, Satyam Sarma, Tanya Martinez-Fernandez, and Olga Gupta for their clinical support. The authors also thank Rubria Marines-Price, Daniel Wilhite, Jonathon Stickford, Vipa Bernhardt, J. Todd Bassett, Raksa Moran, Jessica Alcala, Maria Roman, Joseph Genovese, Andreas Kreutzer, and Anastasia Pyz for their assistance with data collection.
This research was supported by NIH R01 HL136643 and HL096782, Texas Health Presbyterian Hospital Dallas, King Charitable Foundation Trust, and unrestricted funds from Dr. Pepper Snapple.
The results of this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by the American College of Sports Medicine.
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