Estimating cardiorespiratory fitness from heart rates both during and after stepping exercise: a validated simple and safe procedure for step tests at worksites

Feasibility of predicting maximal oxygen uptake by using the efficiency factor in healthy men

Conventionally, efficiency is indirectly estimated through a respiratory gas analyser (oxygen, carbon dioxide), which is a complex and rather costly calculation method that is difficult to perform in many situations. Therefore, the present study proposed a modified definition of efficiency, called the efficiency factor (EF) (i.e., the ratio of work to the corresponding exercise intensity), and evaluated the relation between the EF and maximal oxygen uptake ([Formula: see text]), as well as compared the prediction models established based on the EF. The heart rate (maximal heart rate: 186 ± 6 beats min-1), rating of perceived exertion (19 ± 1), and [Formula: see text] (39.0 ± 7.1 mL kg-1 min-1) of 150 healthy men (age: 20 ± 2 years; height: 175.0 ± 6.0 cm; weight: 73.6 ± 10.7 kg; body mass index [BMI]: 24.0 ± 3.0 kg m-2; percent body fat [PBF]: 17.0 ± 5.7%) were measured during the cardiopulmonary exercise test (CPET). Through multiple linear regression analysis, we established the BMI model using age and BMI as parameters. Additionally, we created the PBF modelHRR utilizing weight, PBF, and heart rate reserve (HRR) and developed PBF modelEF6 and PBF modelEF7 by incorporating EF6 from the exercise stage 6 and EF7 from the exercise stage 7 during the CPET, respectively. EF6 (r = 0.32, p = 0.001) and EF7 (r = 0.31, p = 0.002) were significantly related to [Formula: see text]. Among the models, the PBF modelEF6 showed the highest accuracy, which could explain 62.6% of the variance in the [Formula: see text] at with a standard error of estimate (SEE) of 4.39 mL kg-1 min-1 (%SEE = 11.25%, p < 0.001). These results indicated that the EF is a significant predictor of [Formula: see text], and compared to the other models, the PBF modelEF6 is the best model for estimating [Formula: see text].

Interventions to Address Cardiovascular Risk in Obese Patients: Many Hands Make Light Work

Obesity is a growing public health epidemic worldwide and is implicated in slowing improved life expectancy and increasing cardiovascular (CV) risk; indeed, several obesity-related mechanisms drive structural, functional, humoral, and hemodynamic heart alterations. On the other hand, obesity may indirectly cause CV disease, mediated through different obesity-associated comorbidities. Diet and physical activity are key points in preventing CV disease and reducing CV risk; however, these strategies alone are not always sufficient, so other approaches, such as pharmacological treatments and bariatric surgery, must support them. Moreover, these strategies are associated with improved CV risk factors and effectively reduce the incidence of death and CV events such as myocardial infarction and stroke; consequently, an individualized care plan with a multidisciplinary approach is recommended. More precisely, this review explores several interventions (diet, physical activity, pharmacological and surgical treatments) to address CV risk in obese patients and emphasizes the importance of adherence to treatments.

Estimation methods to detect changes in cardiorespiratory fitness due to exercise training and subsequent detraining

PURPOSE

To determine whether estimated maximal oxygen consumption ([Formula: see text]) can detect cardiorespiratory fitness (CRF) changes by behavioral modifications. This study compared changes in measured [Formula: see text]O_2max (m[Formula: see text]O_2max) through exercise intervention with e[Formula: see text]O_2max using a multiple regression model (MRM) and linear extrapolation method (LEM).

METHODS

A cross-sectional analysis involving 173 adults was conducted to establish an MRM by including age, sex, body mass index, questionnaire score, heart rate (HR) from step test, and m[Formula: see text]O_2max. Subsequently, 15 men participated in an intervention experiment comprising an 8-week, high-intensity interval training, followed by 8-week detraining, and completed anthropometric measurements, questionnaires, step tests, and m[Formula: see text]O_2max tests. m[Formula: see text]O_2max changes throughout the intervention were compared to e[Formula: see text]O_2max changes calculated using the MRM and LEM. The LEM used the HR during the step test with constant values (predetermined [Formula: see text]O₂), such as the Chester step test.

RESULTS

Inclusion of the step test HR in a questionnaire-based MRM improved the estimation power, although the MRM underestimated higher m[Formula: see text]O_2max values. In the intervention, m[Formula: see text]O_2max increased by 20.0 ± 14.1% (P < 0.01) and subsequently decreased by 9.5 ± 6.6% (P < 0.01) after exercise training and detraining, respectively. Significant method × time interactions were observed between m[Formula: see text]O_2max and e[Formula: see text]O_2max in the MRM but not in the LEM, i.e., an apparent systematic error (underestimation of high values) of the MRM was absent in the LEM, although the correlation between m[Formula: see text]O_2max and e[Formula: see text]O_2max using the LEM was moderate.

CONCLUSION

e[Formula: see text]O_2max, particularly using the MRM with HR as an explanatory factor, is not an appropriate method for detecting CRF changes along with behavioral modifications.

CLINICAL TRIAL REGISTRATION

Registered number, UMIN000041031; Registered date, 2020/07/08; URL, https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046855.

Improved VO2max Estimation by Combining a Multiple Regression Model and Linear Extrapolation Method

Maximal oxygen consumption (VO_2max) is an important health indicator that is often estimated using a multiple regression model (MRM) or linear extrapolation method (LEM) with the heart rate (HR) during a step test. Nonetheless, both methods have inherent problems. This study investigated a VO_2max estimation method that mitigates the weaknesses of these two methods. A total of 128 adults completed anthropometric measurements, a physical activity questionnaire, a step test with HR measurements, and a VO_2max treadmill test. The MRM included step-test HR, age, sex, body mass index, and questionnaire scores, whereas the LEM included step-test HR, predetermined constant VO₂ values, and age-predicted maximal HR. Systematic differences between estimated and measured VO_2max values were detected using Bland-Altman plots. The standard errors of the estimates of the MRM and LEM were 4.15 and 5.08 mL·kg^-1·min^-1, respectively. The range of 95% limits of agreement for the LEM was wider than that for the MRM. Fixed biases were not significant for both methods, and a significant proportional bias was observed only in the MRM. MRM bias was eliminated using the LEM application when the MRM-estimated VO_2max was ≥45 mL·kg^-1·min^-1. In conclusion, substantial proportional bias in the MRM may be mitigated using the LEM within a limited range.

The Determination of Step Frequency in 3-min Incremental Step-in-Place Tests for Predicting Maximal Oxygen Uptake from Heart Rate Response in Taiwanese Adults

The maximal oxygen uptake (VO_2max) prediction models established by step tests are often used for evaluating cardiorespiratory fitness (CRF). However, it is unclear which type of stepping frequency sequence is more suitable for the public to assess the CRF. Therefore, the main purpose of this study was to test the effectiveness of two 3-min incremental step-in-place (3MISP) tests (i.e., 3MISP_30s and 3MISP_60s) with the same total number of steps but different step-frequency sequences in predicting VO_2max. In this cross-sectional study, a total of 200 healthy adults in Taiwan completed 3MISP_30s and 3MISP_60s tests, as well as cardiopulmonary exercise testing. The 3MISP_30s and 3MISP_60s models were established through multiple stepwise regression analysis by gender, age, percent body fat, and 3MISP-heart rate. The statistical analysis included Pearson's correlations, the standard errors of estimate, the predicted residual error sum of squares, and the Bland-Altman plot to compare the measured VO_2max values and those estimated. The results of the study showed that the exercise intensity of the 3MISP_30s test was higher than that of the 3MISP_60s test (% heart rate reserve (HRR) during 3MISP_30s vs. %HRR during 3MISP_60s = 81.00% vs. 76.81%, p < 0.001). Both the 3MISP_30s model and the 3MISP_60s model explained 64.4% of VO_2max, and the standard errors of the estimates were 4.2043 and 4.2090 mL·kg^-1·min^-1, respectively. The cross-validation results also indicated that the measured VO_2max values and those predicted by the 3MISP_30s and 3MISP_60s models were highly correlated (3MISP_30s model: r = 0.804, 3MISP_60s model: r = 0.807, both p < 0.001). There was no significant difference between the measured VO_2max values and those predicted by the 3MISP_30s and 3MISP_60s models in the testing group (p > 0.05). The results of the study showed that when the 3MISP_60s test was used, the exercise intensity was significantly reduced, but the predictive effectiveness of VO_2max did not change. We concluded that the 3MISP_60s test was physiologically less stressful than the 3MISP_30s test, and it could be a better choice for CRF evaluation.

Association of cardiorespiratory fitness with the risk factors of cardiovascular disease: Evaluation using the Japan step test from the National Institute of Occupational Safety and Health

OBJECTIVE

Cardiorespiratory fitness (CRF) is an important factor for evaluating cardiovascular disease (CVD) risk. We recently developed a novel method (National Institute of Occupational Safety and Health, Japan step test [JST]) for evaluating CRF in workers; its criterion validity has been investigated. However, its association with health risk must be confirmed before its application in the workplace. Therefore, we aimed to determine the association of JST-evaluated CRF with the prevalence of CVD risk among Japanese workers.

METHODS

For CRF evaluation, working adults completed the JST, which comprised a 3-minute stepping exercise and a 2-minute recovery period. Data on CVD risk factors and clinical history were collected through medical certification within 1 year from the date of the JST measurements. Participants were divided into three groups for multiple logistic regression analyses based on the JST values (low, moderate, and high). Odds ratios (ORs) for the prevalence of CVD risk were calculated.

RESULTS

We recruited 885 working adults (46.4% women). The prevalence of CVD risk in the total population was 18.6%. When compared to the reference group (low CRF), the ORs for CVD risk prevalence after adjustments for lifestyle factors (smoking status, alcohol consumption status, and exercise habits) were 0.42 (95% confidence interval [CI], 0.28-0.63) and 0.29 (95% CI, 0.18-0.45) for the moderate and high groups, respectively.

CONCLUSION

An inverse association was noted between the JST-evaluated CRF and CVD risk prevalence. JST may be helpful for identifying workers at risk for CVD development.

Development and Validation of 3 Min Incremental Step-In-Place Test for Predicting Maximal Oxygen Uptake in Home Settings: A Submaximal Exercise Study to Assess Cardiorespiratory Fitness

The purpose of this research was to develop the 3 min incremental step-in-place (3MISP) test for predicting maximal oxygen uptake (V.O_2max). A total of 205 adults (20–64 years) completed the 3MISP and V.O_2max tests. Using age, gender, body composition (BC) including percent body fat (PBF) or body mass index (BMI), and with or without heart rate (HR) at the beginning of exercise (HR0) or difference between HR at the third minute during the exercise and the first minute post exercise (ΔHR3 − HR4) in the 3MISP test, six V.O_2max prediction models were derived from multiple linear regression. Age (r = −0.239), gender (r = 0.430), BMI (r = −0.191), PBF (r = −0.706), HR0 (r = −0.516), and ΔHR3 − HR4 (r = 0.563) were significantly correlated to V.O_2max. Among the six V.O_2max prediction models, the PBF model^{∆HR3 − HR4} has the highest accuracy. The simplest models with age, gender, and PBF/BMI explained 54.5% of the V.O_2max in the PBF model^BC and 39.8% of that in the BMI model^BC. The addition of HR0 and ∆HR3 − HR4 increases the variance of V.O_2max explained by the PBF and BMI models^{∆HR3 − HR4} by 17.98% and 45.23%, respectively, while standard errors of estimate decrease by 10.73% and 15.61%. These data demonstrate that the models established using 3MISP-HR data can enhance the accuracy of V.O_2max prediction.