Cardiorespiratory fitness (CRF) is a strong and independent predictor of morbidity and all-cause mortality beyond traditional risk factors (1). However, the direct measurement of CRF is not feasible in clinical settings where cost, time, training, and discomfort on behalf of the patient have all been cited as barriers to routine integration (2,3).
An alternative to measuring CRF is to estimate it using a nonexercise CRF equation. Nonexercise equations have been validated against objective measures of CRF and have been shown to correctly classify individuals into both high and low CRF categories (4). Recently, Jackson and colleagues (5) showed that nonexercise estimated CRF (eCRF) could be used to follow change in measured CRF (mCRF) associated with aging. However, it is unknown whether eCRF can be used to estimate change in mCRF following the adoption of regular exercise. Also unknown is whether eCRF can be used to estimate change in mCRF independent of amount and intensity of exercise.
In this study, we sought to determine whether change in eCRF could be used to estimate change in mCRF following the adoption of different amounts and intensities of exercise. We compared change in eCRF derived using the nonexercise equation by Nes et al. (4) to change in mCRF obtained in a previously completed 24-wk exercise intervention, in which exercise groups differed in the amount and intensity of exercise performed. The equation by Nes et al. (4) has been cross-validated in a large heterogeneous population of healthy men and women, is a strong predictor of mortality (6), and incorporates a physical activity (PA) score that distinguishes between amount and intensity of exercise performed. However, whether eCRF derived using this equation can follow exercise-induced changes in mCRF is unknown.
Study setting and participants
Details of the trial design and the primary findings have been published elsewhere (7,8). Briefly, we conducted a 24-wk, single-center, randomized controlled trial with a parallel group design between September 1, 2009, and May 31, 2013. The primary objective of the original investigation was to determine the separate effects of exercise amount and intensity on waist circumference (WC) and glucose tolerance among 300 sedentary adults with abdominal obesity. Potential participants were excluded if they reported a history of heart disease, stroke, or any condition that would prevent them from engaging in exercise, if they were already engaged in two or more planned exercise sessions per week, and if they had diabetes. All participants provided written informed consent before participation, and the study was originally approved by the Queen’s University Health Sciences Research Ethics Board. Of the 300 participants originally randomized, participant data were excluded if participants in the exercise groups did not complete a minimum of 90% of exercise sessions (n = 104) and did not have V˙O2peak data at baseline and follow-up (n = 33). This resulted in a study sample of 163 participants.
The subsample of 163 participants were originally randomized to the following: 1) no-exercise control (n = 42); 2) low-amount, low-intensity exercise (LALI; n = 39); 3) high-amount, low-intensity exercise (HALI; n = 51); or 4) high-amount, high-intensity exercise (HAHI; n = 31). All participants in the exercise groups performed primarily walking exercise on a treadmill for the time required to achieve the desired energy expenditure (kilocalories per session) five times per week at the required intensity (relative to CRF [V˙O2peak]) for 24 wk. Using heart rate and oxygen consumption data obtained from the baseline fitness test (V˙O2peak), the heart rate associated with an oxygen consumption of approximately 50% (LALI and HALI) and 75% (HAHI) was prescribed for each participant. At these exercise intensities, the energy expenditure targets (exercise amount) were 180 and 300 kcal for women and men, respectively, in the LALI group, and 360 and 600 kcal for women and men, respectively, for the HALI and HAHI groups. Energy expenditure targets for the low-amount group were prescribed so that this energy would be expended in approximately 30 min to conform to minimum PA guidelines (7,9). Energy expenditure targets for the HALI group (matched for intensity with LALI) were designed such that this energy would be expended in approximately twice the amount of time as the LALI group (~60 min) (7). Heart rate was monitored continuously for all exercise participants at every session to help ensure adherence to the prescribed exercise intensity. All exercise sessions were performed under supervision.
mCRF (V˙O2peak) was assessed at baseline, 4, 8, 16, and 24 wk using standard open-circuit spirometry techniques with a mass flow sensor (SensorMedics, Yorba Linda, CA) during a graded exercise test. In the test, participants walked on a treadmill at a self-selected speed at zero elevation for 3 min, after which the incline was increased by 2% every subsequent 3 min until volitional fatigue (7). We considered a V˙O2peak test to be valid if the test met at least three of the following criteria: (i) a plateau in oxygen consumption (<2 mL·kg−1⋅min−1 increase) with increasing work rate (increasing treadmill incline, speed, or both), (ii) the RER exceeded a value of 1.10, (iii) the heart rate of the participant exceeded their age-predicted maximum (220 − age), and/or (iv) the participant achieved maximum effort (Borg scale = 10).
eCRF was calculated using the equation developed by Nes et al. in 2011 (4). There are separate equations for men and women that were used in this analysis accordingly:
Nonexercise equation for women:
Nonexercise equation for men:
The PA index used in the nonexercise equation was adapted from the index originally derived by Kurtze et al. (10). This index consists of three components: frequency, amount, and intensity of exercise (Table 1). A PA index score is derived by calculating the product of the scores for each of amount, intensity, and frequency of exercise performed.
An index score was derived for each exercise group to reflect the actual exercise performed throughout the trial while also distinguishing between low and high amount and intensity of exercise prescribed. Specifically, the LALI group was characterized by an exercise amount of 16–30 min per session and an exercise intensity of “heavy breath and sweat.” The HALI group was characterized by an exercise amount of 30–60 min per session and an exercise intensity of “heavy breath and sweat.” The HAHI group was characterized by an exercise amount of 30–60 min per session and an exercise intensity of “push near exhaustion.” All exercise groups were characterized by a frequency of “almost every day.” This resulted in a PA index score of 15, 22.5, and 45 for LALI, HALI, and HAHI, respectively (Table 1).
WC was measured in duplicate at the superior edge of the iliac crest (11) at baseline, 8, 16, and 24 wk. Weight was measured using the same calibrated beam scale throughout the trial. There were separate assessment and intervention personnel, and all assessment personnel were blinded to participant randomization assignment.
Resting heart rate
Resting heart rate (RHR) was measured before each CRF test. Participants wore a heart rate monitor and were asked to sit quietly in a comfortable chair for 2–5 min until the lowest heart rate within that time frame was recorded. The average RHR recorded was 89.7 ± 12.8 bpm. This average value was substantially higher than the average RHR reported in the 2009–2011 Canadian Health Measures Survey (12) for men (67 bpm) and women (68 bpm) between the ages of 40–59 yr. Use of our measured RHR value led to a systematic underestimation of mCRF at all time points for all groups except HAHI (data not shown). As such, we decided to use the RHR measures from the Canadian Health Measures Survey for our analysis.
A Kruskal–Wallis test was performed to compare the following baseline characteristics between intervention groups: age, weight, WC, BMI, mCRF, and eCRF. A Kruskal–Wallis test was performed to compare exercise adherence at 24 wk and change in WC from baseline to 8, 16, and 24 wk between intervention groups. A Dunn–Bonferroni post hoc test was performed to further identify significance when necessary. A Pearson chi-square test was used to compare sex between groups. Linear regression was used to examine the interaction of sex on change in eCRF. No interaction was observed; thus, participant data were collapsed across sex. A Friedman nonparametric test was performed to compare differences across time (baseline to 8, 16, and 24 wk) within groups for eCRF and mCRF. Wilcoxon signed rank tests were performed to further identify significance for the Friedman tests where necessary. Wilcoxon signed rank tests were performed to compare eCRF and mCRF at baseline, 8, 16, and 24 wk, and changes in eCRF and mCRF from baseline to 8, 16, and 24 wk. To identify whether there was proportional systematic variation between change in mCRF and change in eCRF, linear regression was used to regress the mean difference between change in mCRF and change in eCRF on change in mCRF at 24 wk. Wilcoxon signed rank tests were additionally performed to compare changes in eCRF and mCRF at 24 wk using six other nonexercise equations (5,13–17) (see Table, Supplemental Digital Content, Formulas for the six additional nonexercise equations included in the analysis of change in estimated vs measured CRF over 24 wk, http://links.lww.com/MSS/B916). Statistical significance was adjusted to P < 0.02 using a Bonferroni correction for three comparisons in each family of tests. All statistical analyses were performed using SPSS 25.0 software (SPSS, Chicago, IL).
There were no differences in baseline characteristics between intervention groups (Table 2; P = 0.33). There were no differences between eCRF and mCRF at baseline within groups (P = 0.11). On average, participants attended 115 of the 120 exercise sessions prescribed (96.0% ± 4.0% adherence). There was no difference in exercise adherence between groups (P = 0.79).
eCRF compared with mCRF from baseline to 24 wk
There was a significant increase in mCRF and eCRF in all exercise groups at all time points (baseline to 8, 16, and 24 wk; P < 0.001). eCRF was not different from mCRF at any time for any group (P = 0.21), except HAHI, in which eCRF was significantly higher than mCRF at 8 and 16 wk (Table 3; P = 0.01). When the lower exercise intensity value (PA index score = 22.5 using intensity of “heavy breath and sweat”; Table 1) was used in the eCRF equation to derive eCRF for HAHI, eCRF was no longer different from mCRF at 8 and 16 wk (P = 0.03) but was now significantly lower than mCRF at 24 wk (P = 0.002; Table 3).
Change in eCRF compared with change in mCRF
Change in eCRF from baseline to 8, 16, and 24 wk was not different from change in mCRF for control, LALI, and HALI (Table 3 and Fig. 1; P = 0.03). In HAHI, change in eCRF was significantly higher than change in mCRF at all time points (baseline to 8, 16, and 24 wk; P < 0.001). When the lower exercise intensity value was used in the eCRF equation for HAHI, change in eCRF was no longer different from mCRF at 8 and 16 wk (P = 0.02) but was now significantly lower than change in mCRF at 24 wk (P = 0.001; Table 3).
Change in WC over 24 wk
The change in WC from baseline to 8, 16, and 24 wk was not different between exercise groups (P = 0.17) but was different between all exercise groups and control (8 wk: control data N/A, −2.6 ± 3.0, −3.2 ± 2.5, −3.8 ± 3.3; 16 wk: −1.7 ± 2.9, −4.3 ± 4.5, −5.1 ± 3.7, −5.6 ± 45; 24 wk: −1.2 ± 4.4, −5.6 ± 5.2, −6.2 ± 3.8, −7.3 ± 5.0, for control, LALI, HALI, HAHI, respectively; P < 0.001).
Systematic variation between change in mCRF and change in eCRF at 24 wk
To determine whether there were systematic differences between change in mCRF and change in eCRF at 24 wk, Bland–Altman plots were constructed using the mean difference between change in mCRF and change in eCRF plotted against change in mCRF (Fig. 2). The 95% limits of agreement were beyond the clinical threshold of ±1 MET in all groups, suggesting that changes in eCRF and mCRF could not be used interchangeably for a given individual. The mean difference between the two measures was then regressed on change in mCRF. A significant relationship was identified between the mean difference and change in mCRF for all groups: control (P < 0.001; 95% confidence interval [CI] = 0.82–1.08), LALI (P < 0.001; 95% CI = 0.51–0.79), HALI (P < 0.001; 95% CI = 0.72–0.93), and HAHI (P < 0.001; 95% CI = 0.54–0.85).
Change in eCRF compared with change in mCRF using other nonexercise equations
Change in mCRF was compared with change in eCRF at 24 wk using six additional nonexercise equations (Table 4). Changes in eCRF and mCRF were not different within the control group for all nonexercise equations (P ≥ 0.36). None of the equations were good estimates of change in mCRF for all three exercise groups. Two equations were different in only one exercise group: change in eCRF using the equation by Matthews et al. (15) was significantly different from change in mCRF for HAHI (P < 0.001), and using the equation by Jurca et al. (17) was significantly different from change in mCRF for HALI (P < 0.001). In four equations (Jackson et al., 1990 (), Whaley et al. 1995 (), Wier et al. 2006 (), and Jackson et al., 2012 ()), changes in eCRF and mCRF were significantly different in two exercise groups (P ≤ 0.01). As a result, the equation by Matthews et al. (15) was the only equation associated with change in mCRF independent of amount, and none were associated independent of intensity.
A primary observation of this investigation is that eCRF was associated with change in mCRF at 24 wk independent of exercise amount but not intensity. Our observation that systematic variation exists between eCRF and mCRF highlights a principal limitation when using eCRF to follow exercise-induced change in mCRF. The nonexercise equation does not capture the well-established and substantial individual variability of the CRF response to standardized exercise that exists independent of age and biological sex (19).
We are aware of a single study that investigated whether a nonexercise equation could estimate change in mCRF; in that study, the authors used longitudinal data to show that eCRF predicted change in mCRF associated with aging over a 30-yr period (5). Although we chose not to use their equation in our analysis, as their PA score does not distinguish exercise based on intensity, we did explore how their equation compared with the Nes et al. equation. We observed that the Jackson et al. eCRF model led to a similar systematic over and underestimation of mCRF (data not shown). In addition, we found that eCRF was not associated with change in mCRF independent of amount or intensity.
When we analyzed an additional five equations (13–15,17,20) and calculated change in eCRF per group at 24 wk, we found that only one equation (15) derived eCRF estimates that were associated with change in mCRF independent of amount, and none were associated with change in mCRF independent of intensity (Table 4). The PA scales of these five additional equations were organized in such a way as to represent the regular PA habits of individuals (e.g., sedentary vs participates regularly in endurance exercise vs highly trained) but did not allow for much flexibility in selecting specific amounts and intensities of exercise. In our hands, this made it difficult to accurately represent the different amounts and intensities of our exercise groups, which may have led to an inaccurate representation of the effect of PA on change in eCRF across our groups. Separating the PA score into distinct components of amount, intensity, and frequency of PA (as with Nes et al.) may improve the ability to capture different exercise regimes and therefore may improve the accuracy by which nonexercise equations estimate the average change in mCRF.
However, even with the Nes et al. equation, we were unable to accurately estimate change in mCRF independent of exercise intensity. This is a reflection of the difficulty involved in accurately representing the relationship between exercise intensity and CRF. The exercise intensity category we used to derive eCRF for our high-intensity group (using the Nes et al. PA intensity category of “push near exhaustion”) was likely an overestimation of the intensity that our participants performed (vigorous intensity, ~75% V˙O2peak) and may more closely represent the effort associated with high-intensity training (~85%–90% V˙O2peak). However, when we used a lower-intensity category (“heavy breath and sweat”) to calculate eCRF, change in eCRF was significantly lower than mCRF at 24 wk. Given the profound effect that small changes in exercise intensity have on mCRF (8,21), the addition of more intensity categories to the PA score used by Nes et al. would likely improve the precision of this equation in estimating change in mCRF at the group level.
A principal limitation inherent to all nonexercise equations is that they cannot capture the substantial individual variability of mCRF responses. It is now well established that in response to a standardized dose of exercise, there will be substantial individual variability in the mCRF response (19). However, with the eCRF equation, there is no variability in response to exercise because all participants within an exercise group will increase their eCRF by the same amount in response to the same PA score. This lack of variability in eCRF change is underscored by the fact that two of the remaining variables within the eCRF equation do not change (age and gender) and two others change very little (WC and RHR). For example, RHR changes by approximately 6.0% in response to exercise training (18), which, using the Nes equation, equates to only an ~0.5 mL·kg−1⋅min−1 change in eCRF. In our hands, participants observed a reduction in WC that approximated 6 cm, which equates to an ~2 mL·kg−1⋅min−1 change in eCRF. However, given the environmental challenges that exist in reducing weight and/or WC, in unsupervised conditions, change in WC would likely be substantially less (22) and, thus, contribute far less to eCRF. The result is that the change in eCRF for a given dose of exercise is relatively homogeneous. Because the variables in the Nes et al. equation are the major determinants of CRF and consequently, common to eCRF equations (5,13,14,16,17,20,23–25), the ability of any CRF estimate to capture low or high CRF responders for any dose of exercise is unlikely.
It is important to note, however, that the inability of eCRF to identify extremes in the variability of the CRF response to exercise is a limitation that pales in comparison with the benefit of routine estimation of CRF in clinical settings. In other words, although it is true that eCRF measures may over- or underestimate the “true” CRF response, that practitioners would incorporate eCRF as a routine measure provides unique opportunities to counsel patients on CRF, which is unambiguously associated with risk (1). In addition, as PA is the primary determinant of CRF this tool provides the opportunity to counsel the patient with respect to PA behavior, which is itself associated with benefit across a wide range of outcomes (26,27). For these reasons alone, eCRF should be integrated into clinical settings to improve patient management and reduce long-term risk.
Strengths of our study include analysis of the effect of different amounts and intensities of exercise performed on the association between eCRF and mCRF. mCRF was measured at regular intervals over the course of the intervention, which allowed us to explore whether change in eCRF was associated with change in mCRF over 24 wk. In this analysis, we only included participants who completed a minimum of 90% of exercise sessions, providing confidence that change in mCRF was due to the exercise as prescribed. Although our findings are in individuals with overweight or obesity, that 60%–70% (28,29) of the North American population has overweight or obesity suggests that our findings are highly generalizable. A limitation of this study is that we took single measures of V˙O2peak at each time point; previous groups have reported considerable day-to-day variability in V˙O2peak values such that it is now recommended that a minimum of two V˙O2peak tests be taken for each participant at each time point (19,30). The exercise habits of most individuals are likely more variable than the interventions followed by our participants. Thus, for any individual engaging in various intensities and amounts of exercise on a regular basis, they would have to select the PA activity score that best reflects their average level of activity. This may limit the accuracy of the nonexercise equation in capturing all PA habits. However, for those looking to increase their CRF, this equation and the associated PA index may be useful as a simple tool to estimate the amount of PA required to improve CRF. For example, if a sedentary male wanted to improve his eCRF by 1 MET (3.5 mL·kg−1⋅min−1), he could do so by achieving a PA index score of 15, equivalent to exercising almost every day, at a moderate intensity, for 16–30 min. Finally, an additional limitation of our study is the use of a reference value for RHR instead of our measured value, which may have influenced the variability of our eCRF measures, albeit modestly. Health care practitioners looking to incorporate an eCRF tool into their practice should encourage patients to measure RHR upon waking to improve the accuracy of this component of the equation. Alternatively, practitioners may prefer to use an estimation equation that does not include RHR, of which there are several (13,15,20,24,25,31–34).
Criterion measurement of CRF in clinical settings is unlikely to occur given obstacles, including cost and time. The challenges addressed here when using a nonexercise equation to estimate CRF are far outweighed by the opportunity this tool provides to estimate CRF and, in so doing, monitor the PA behavior of patients over time. This is encouraging given the established associations of CRF and PA with cardiovascular morbidity and all-cause mortality risk.
This work was supported by the Canadian Institutes of Health Research (grant no. OHN-63277; http://www.cihr-irsc.gc.ca). The results of the present study do not constitute endorsement by the American College of Sports Medicine. The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation.
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