Nonexercise Equations for Cardiorespiratory Fitness in Older Adults using Body Roundness Index and Waist...

Cardiorespiratory fitness (CRF), quantified by oxygen consumption at maximal exercise (VO_2max), is an important indicator of general health status with aging and related chronic diseases. CRF prediction equations may be an acceptable form of estimating associated health risks when VO_2max cannot be measured. The purpose of this study was to describe new CRF prediction equations that utilize waist circumference (WC) and body roundness index (BRI) to improve CRF estimation in inactive older adults.

Secondary cross-sectional analyses of baseline data from two randomized controlled trials (Exercise Effects on Brain Health and Learning from Minutes to Months [EXTEND], n = 113; Effects of a Bicycling Intervention on Cognitive Skills and Cardiovascular Health [BIKE], n = 32) included inactive older adults without dementia aged 55-80 yr without psychiatric or cardiovascular diseases. Height, weight, WC, BRI, and body mass index were measured for each participant. Participants also completed the short-form International Physical Activity Questionnaire (IPAQ), the Self-Report Physical Activity Survey (SRPAS), and a maximal exercise test to determine VO_2max.

Four nonexercise equations estimating CRF were created using linear regression with the variables sex, age, and a combination of IPAQ and WC (eCRF1: R ² = 0.67, standard error [SE] = 4.18), SRPAS and WC (eCRF2: R ² = 0.63, SE = 6.49), SRPAS and BRI (eCRF3: R ² = 0.63, SE = 5.62), or IPAQ and BRI (eCRF4: R ² = 0.68, SE = 3.46) from EXTEND participants. When validated on out-of-sample BIKE participants, the four new equations were more highly correlated to measured VO_2max than previously established models (eCRF1: R ² = 0.45, constant error [CE] = 0.59; eCRF2: R ² = 0.42, CE = 1.30; eCRF3: R ² = 0.47, CE = 1.78; eCRF4: R ² = 0.52, CE = 0.84).

Equations using WC and BRI perform better for estimating CRF than equations using body mass index and resting heart rate in an inactive older adult population. These equations could be used to screen participants during study enrollment when specific estimates of CRF are desired for the study sample. However, these equations should be validated for use in clinical populations to stratify disease risk.