Prediction Of Energy Expenditure Of Physical Activities During Balneotherapy Using Heart Rates

While balneotherapy has been widely used in both health promotion and physical therapy, measuring energy expenditure (EE) of physical activity (PA) during the balneotherapy has been a challenge due to most of EE measurement devices were not waterproof. PURPOSE: To use a waterproof arm heart rate monitor (WAHR) to estimate EE of PA during balneotherapy. METHOD: 20 young adult participants (Male% =50% ; M ± SD: Age = 25 ± 2.2, Height = 167.5 ± 7.9, Weight = 61.9 ± 10.7, & BMI = 22.0 ± 3.0) were recruited to measure their EE by K5 (CosMed), HR with Polar HR and WAHR monitors during a graded exercise intensity protocol (5-min warm-up, 5-min HRmax 60-69%, 10-min HRmax 70-19%, 3-min HRmax 80-95% & 5-min cooling down), both on a land treadmill and on a bike in a balneotherapy pool with temperatures between 36.5 and 37.2 C°. Correlations and differences between two HR monitors and their predictions of EE, with and without individual calibration, were computed and compared with each other and with HR based EE predictions by Keytel et al. (2005). RESULTS: Correlations between two monitors were high both in land and pool, but there were absolute differences. HR from both HR monitors had a moderate correlation with EE and, with (IC) and without (No) individual calibration, a large range of prediction variances were observed. Finally, Keytel equation can predict EE moderately well in both land and pool conditions: - Correlation(r) Differences(t-value, EE, p-value) Polar WAHR Keytel IC No IC No M F Land 0.81 -11.1, 0.14, <0.05 0.59 0.59 0.27 0.27 0.59 0.57 Pool 0.78 -4.2, 0.07, <0.05 0.50 0.50 0.43 0.43 0.47 0.57 CONCLUSION: HR can only moderately well in predicting EE in both land and pool conditions. Further efforts are needed to include other variables in prediction, as well explore other statistical and AI methods.