Key result
Adding BMI, age, and sex to heart rate reduces wVO2 estimation error by ~36%.
Why the study?
Heart rate-based estimates of VO2 may be inaccurate after stroke due to changes in the heart rate–VO2 relationship, leaving the accuracy of estimating relative walking VO2 and exercise intensity unclear.
Does incorporating clinical variables (BMI, age, sex) into heart rate-based models improve the accuracy of wVO2 estimations in patients post-stroke?
Cross-Sectional (n=16)
None
Does incorporating clinical variables (BMI, age, sex) into heart rate-based models improve the accuracy of wVO2 estimations in patients post-stroke?
Effect estimate: MAE Δ: -36.01%
Absolute Event Rate: 1.99% vs 3.11%
Incorporating easily obtainable clinical variables like BMI, age, and sex significantly enhances the accuracy of heart rate-based energy expenditure models for stroke rehabilitation.
May refine wVO2 estimates for post-stroke exercise monitoring; leaves open prospective validation before practice change.
Background: Indirect calorimetry is the gold standard field-testing technique for measuring energy expenditure and exercise intensity based on the volume of oxygen consumed (VO2, mL O2/min). Although heart rate is often used as a proxy for VO2, heart rate-based estimates of VO2 may be inaccurate after stroke due to changes in the heart rate–VO2 relationship. Our objective was to evaluate in people post stroke the accuracy of using heart rate to estimate relative walking VO2 (wVO2) and classify exercise intensity. Moreover, we sought to determine if estimation accuracy could be improved by including clinical variables related to patients’ function and health in the estimation. Methods: Sixteen individuals post stroke completed treadmill walking exercises with concurrent indirect calorimetry and heart rate monitoring. Using 70% of the data, forward selection regression with repeated k-fold cross-validation was used to build wVO2 estimation equations that use heart rate alone and together with clinical variables available at the point-of-care (i.e., BMI, age, sex, and comfortable walking speed). The remaining 30% of the data were used to evaluate accuracy by comparing (1) the estimated and actual wVO2 measurements and (2) the exercise intensity classifications based on metabolic equivalents (METs) calculated using the estimated and actual wVO2 measurements. Results: Heart rate-based wVO2 estimates were inaccurate (MAE = 3.11 mL O2/kg/min) and unreliable (ICC = 0.68). Incorporating BMI, age, and sex in the estimation resulted in improvements in accuracy (MAE Δ: −36.01%, MAE = 1.99 mL O2/kg/min) and reliability (ICC Δ: +20, ICC = 0.88). Improved exercise intensity classifications were also observed, with higher accuracy (Δ: +29.85%, from 0.67 to 0.87), kappa (Δ: +108.33%, from 0.36 to 0.75), sensitivity (Δ: +30.43%, from 0.46 to 0.60), and specificity (Δ: +17.95%, from 0.78 to 0.92). Conclusions: In people post stroke, heart rate-based wVO2 estimations are inaccurate but can be substantially improved by incorporating clinical variables readily available at the point of care.
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Cataldo et al. (2024) conducted a cross-sectional in Chronic Stroke (n=16). wVO2 estimation incorporating heart rate, BMI, age, and sex vs. wVO2 estimation using heart rate alone was evaluated on Mean absolute error (MAE) of relative walking VO2 (wVO2) estimation (MAE Δ: -36.01%). An estimation equation incorporating BMI, age, and sex alongside heart rate reduced the mean absolute error of wVO2 estimations by 36% compared to using heart rate alone.
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