Open AccessAccuracy of Nonexercise Prediction Equations for Assessing Longitudinal Changes to Cardiorespiratory Fitness in Apparently Healthy Adults: BALL ST Cohort
Author(s) -
Peterman James E.,
Harber Matthew P.,
Imboden Mary T.,
Whaley Mitchell H.,
Fleenor Bradley S.,
Myers Jonathan,
Arena Ross,
Finch W. Holmes,
Kaminsky Leonard A.
Publication year - 2020
Publication title -
journal of the american heart association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.494
H-Index - 85
ISSN - 2047-9980
DOI - 10.1161/jaha.119.015117
Subject(s) - cardiorespiratory fitness , intraclass correlation , medicine , cohort , correlation , pearson product moment correlation coefficient , generalized estimating equation , physical therapy , mathematics , statistics , psychometrics , clinical psychology , geometry
Background Repeated assessment of cardiorespiratory fitness ( CRF ) improves mortality risk predictions in apparently healthy adults. Accordingly, the American Heart Association suggests routine clinical assessment of CRF using, at a minimum, nonexercise prediction equations. However, the accuracy of nonexercise prediction equations over time is unknown. Therefore, we compared the ability of nonexercise prediction equations to detect changes in directly measured CRF . Methods and Results The sample included 987 apparently healthy adults from the BALL ST (Ball State Adult Fitness Longitudinal Lifestyle Study) cohort (33% women; average age, 43.1±10.4 years) who completed 2 cardiopulmonary exercise tests ≥3 months apart (3.2±5.4 years of follow‐up). The change in estimated CRF ( eCRF ) from 27 distinct nonexercise prediction equations was compared with the change in directly measured CRF . Analysis included Pearson product moment correlations, SEE values, intraclass correlation coefficient values, Cohen's κ coefficients, γ coefficients, and the Benjamini‐Hochberg procedure to compare eCRF with directly measured CRF . The change in eCRF from 26 of 27 equations was significantly associated to the change in directly measured CRF ( P <0.001), with intraclass correlation coefficient values ranging from 0.06 to 0.63. For 16 of the 27 equations, the change in eCRF was significantly different from the change in directly measured CRF . The median percentage of participants correctly classified as having increased, decreased, or no change in CRF was 56% (range, 39%–61%). Conclusions Variability was observed in the accuracy between nonexercise prediction equations and the ability of equations to detect changes in CRF . Considering the appreciable error that prediction equations had with detecting even directional changes in CRF , these results suggest eCRF may have limited clinical utility.