Anna Roto Cataldo
Anna Roto Cataldo has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
Diana Thomas
Department of Mathematical Sciences, United States Military Academy West Point, West Point, New York
1 shared publication- Hannah Kittrell
Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, New York
1 shared publication - Heyjun Park
Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
1 shared publication - Jie Fei
Department of Physical Therapy, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA 02215, USA
1 shared publication - Julie Ann Pt Starr
Department of Physical Therapy, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA 02215, USA
1 shared publication - Karen J. Hutchinson
Department of Physical Therapy, Sargent College of Health and Rehabilitation Sciences, Boston University, Boston, MA 02215, USA
1 shared publication
Latest Publications
- Open Access
- 10 December 2024
- Published by MDPI AG in Bioengineering
- Vol. 11 (12) , 1250
Enhancing Heart Rate-Based Estimation of Energy Expenditure and Exercise Intensity in Patients Post Stroke
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.
- 10 December 2024
- Published by MDPI AG in Bioengineering
- Vol. 11 (12) , 1250
Open AccessEnhancing Heart Rate-Based Estimation of Energy Expenditure and Exercise Intensity in Patients Post Stroke
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.