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Prediction of functional aerobic capacity without exercise testing

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The purpose of this study was to develop functional aerobic capacity prediction models without using exercise tests (N-Ex) and to compare the accuracy with Astrand single-stage submaximal prediction methods. The data of 2,009 subjects (9.7% female) were randomly divided into validation (N = 1,543) and cross-validation (N = 466) samples. The validation sample was used to develop two N-Ex models to estimate VO2peak. Gender, age, body composition, and self-report activity were used to develop two N-Ex prediction models. One model estimated percent fat from skinfolds (N-Ex %fat) and the other used body mass index (N-Ex BMI) to represent body composition. The multiple correlations for the developed models were R = 0.81 (SE = 5.3 ml.kg-1.min-1) and R = 0.78 (SE = 5.6 ml.kg-1.min-1). This accuracy was confirmed when applied to the cross-validation sample. The N-Ex models were more accurate than what was obtained from VO2peak estimated from the Astrand prediction models. The SEs of the Astrand models ranged from 5.5-9.7 ml.kg-1.min-1. The N-Ex models were cross-validated on 59 men on hypertensive medication and 71 men who were found to have a positive exercise ECG. The SEs of the N-Ex models ranged from 4.6-5.4 ml.kg-1.min-1 with these subjects.(ABSTRACT TRUNCATED AT 250 WORDS)
... It can be assessed by measuring VO 2max in maximum stress tests (direct maximum tests with gas analysis) or estimated with maximum stress tests (indirect maximum tests without gas analysis), submaximal field and clinical tests, and nonexercise prediction equations (Cooper 1968;Bruce et al. 1973;Jackson et al. 1990;Laboratories 2002;Maranhao Neto Gde et al. 2004;Balady et al. 2010;Nes et al. 2011;Jackson et al. 2012;Kaminsky et al. 2019b;Cuenca-Garcia et al. 2022). ...
... Nonexercise prediction equations are an alternative to maximal and submaximal stress tests to estimate CRF in the health setting (Jackson et al. 1990(Jackson et al. , 2012Maranhao Neto Gde et al. 2004;Nes et al. 2011;Kaminsky et al. 2019b;Arcila et al. 2022). These models include physiological variables commonly evaluated that indicate physical fitness, such as: (i) sex, (ii) age, (iii) body mass index, (iv) heart rate at rest, and (v) physical activity performed (objective and subjective) (Jackson et al. 1990;Maranhao Neto Gde et al. 2004;Arcila et al. 2022). ...
... Nonexercise prediction equations are an alternative to maximal and submaximal stress tests to estimate CRF in the health setting (Jackson et al. 1990(Jackson et al. , 2012Maranhao Neto Gde et al. 2004;Nes et al. 2011;Kaminsky et al. 2019b;Arcila et al. 2022). These models include physiological variables commonly evaluated that indicate physical fitness, such as: (i) sex, (ii) age, (iii) body mass index, (iv) heart rate at rest, and (v) physical activity performed (objective and subjective) (Jackson et al. 1990;Maranhao Neto Gde et al. 2004;Arcila et al. 2022). The nonexercise prediction equations to estimate CRF have shown consistent associations with mortality from all causes and of cardiovascular origin and have good discriminatory power and excellent capacity for risk reclassification (Stamatakis et al. 2013;Qiu et al. 2021). ...
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Given their importance in predicting clinical outcomes, cardiorespiratory fitness (CRF) and muscle status can be considered new vital signs. However, they are not routinely evaluated in healthcare settings. Here, we present a comprehensive review of the epidemiological, mechanistic, and practical bases of the evaluation of CRF and muscle status in adults in primary healthcare settings. We highlight the importance of CRF and muscle status as predictors of morbidity and mortality, focusing on their association with cardiovascular and metabolic outcomes. Notably, adults in the best quartile of CRF and muscle status have as low as one-fourth the risk of developing some of the most common chronic metabolic and cardiovascular diseases than those in the poorest quartile. The physiological mechanisms that underlie these epidemiological associations are addressed. These mechanisms include the fact that both CRF and muscle status reflect an integrative response to the body function. Indeed, muscle plays an active role in the development of many diseases by regulating the body’s metabolic rate and releasing myokines, which modulate metabolic and cardiovascular functions. We also go over the most relevant techniques for assessing peak oxygen uptake as a surrogate of CRF and muscle strength, mass, and quality as surrogates of muscle status in adults. Finally, a clinical case of a middle-aged adult is discussed to integrate and summarize the practical aspects of the information presented throughout. Their clinical importance, the ease with which we can assess CRF and muscle status using affordable techniques, and the availability of reference values, justify their routine evaluation in adults across primary healthcare settings.
... A synopsis of key studies that have developed multivariable models to estimate CRF from non-exercise data is shown in Table 1 (Ref. [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]). Several observations are notable from the table. ...
... This degree of association appears to be generally adequate in terms of classifying individuals into CRF categories (e.g., quartiles or quintiles). In real terms, the error between estimated and measured CRF is generally in the range of 5-15% [23][24][25]31,35]. Nes et al. [35] for example, studied >4000 men and women using a non-exercise test model to estimate CRF and reported that >90% of subjects were correctly classified into the lowest and highest quartiles of CRF. ...
... Nes et al. [35] for example, studied >4000 men and women using a non-exercise test model to estimate CRF and reported that >90% of subjects were correctly classified into the lowest and highest quartiles of CRF. The available equations have tended to underestimate CRF among higher fit individuals and overestimate CRF among lower fit individuals [23,24,26,28,31,35]. This is generally not an issue among highly fit individuals who would still be correctly classified into the higher CRF categories but is a potential concern for low fit individuals because correct classification is much more likely to influence their estimation of risk. ...
... Concerning neonatal morbidity, bronchopulmonary dysplasia (BPD) was defined as supplemental oxygen requirements at 36 weeks post-menstrual age [38]. Since exercise could account for differences in irisin levels [39], physical activity of the participants was also assessed by use of Physical Activity Rating (PA-R) questionnaire, providing a grade from 0 to 7 [40]. ...
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Prematurity has been linked with endothelial dysfunction in later life. The purpose of this study was to evaluate the association between plasma irisin, an adipomyokine reported to protect the functional integrity of vascular endothelium, and circulating endothelial microparticles (EMPs) and endothelial progenitor cells (EPCs), consisting early biomarkers of endothelial dysfunction, in preterm-born children. We studied 131 prepubertal children; 61 preterm and 70 born at term (controls). Plasma irisin was determined by ELISA. Circulating CD62E(+), CD144(+) and CD31(+)/CD42b(-) EMPs, and CD34(+)/VEGFR-2(+)/CD45(-) and CD34(+)/VEGFR-2(+)/CD45dim EPCs, were determined by flow cytometry. Body mass index, waist-to-hip ratio, neck circumference, systolic and diastolic blood pressure, and biochemical parameters (glucose, lipids, insulin, HOMA-IR) were also evaluated. Plasma irisin was significantly lower (p = 0.001), whereas circulating EMPs and EPCs were higher, in children born prematurely compared to controls. Irisin was recognized as independent predictor for CD144(+) and CD31(+)/CD42b(-) EMPs, CD34(+)/VEGFR-2(+)/CD45(-) and CD34(+)/VEGFR-2(+)/CD45dim EPCs in the total study population, and for CD31(+)/CD42b(-) EMPs in the preterm group. In conclusion, plasma irisin correlates independently with circulating EMP and EPC subpopulations in prepubertal children and in preterm-born ones. Further studies in children will potentially elucidate the link between irisin and the primary stages of prematurity-related endothelial dysfunction.
... El Cuestionario de Medición de la Capacidad Física sin Hacer Ejercicio, funcionó para conocer el estatus previo de la capacidad funcional y aptitud física. Jackson et al. (1990) identificaron un adecuado valor de predicción respecto a medidas objetivas del , obteniendo un valor R ≥ .78 respecto a grasa en pliegues cutáneos e IMC. ...
... Por otra parte, este trabajo también presenta algunas limitaciones. Con el modelo utilizado, la frecuencia cardíaca en reposo no se asoció con la capacidad física cardiorrespiratoria, lo cual plantea cuestionamientos sobre la veracidad de esta medida, un hallazgo también reportado por otros autores (21,22,34,35). Esta situación podría deberse a la variabilidad de la frecuencia cardiaca en reposo en el consultorio médico. ...
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IT IS generally agreed by work physiologists that the capacity to perform long continued physical work in a temperate environment is related to the capacity of the cardiovascular respiratory system to deliver oxygen to the muscles (the maximal oxygen intake). Since under carefully standardized conditions in selected homogeneous groups the pulse rate at submaximal levels of work is systematically related to the maximal oxygen intake, it follows that the capacity to perform physical work can be estimated from study of the pulse rate at [SEE FIG. 1. IN SOURCE PDF.] submaximal work levels. Work physiologists have employed for this purpose, the response of the pulse rate to a standard task or have measured the amount of work required to elevate the pulse rate to predetermined pulse levels. Work physiologists have been preoccupied with the estimation of the capacity to do physical work.