Predictive Accuracy of the Nelson Equation via BodPod Compared to Commonly Used Equations to Estimate Resting Metabolic Rate in Adults

International Journal of Exercise Science 14(2): 1166-1177, 2021. Indirect calorimetry (IC) is considered the gold standard for assessing resting metabolic rate (RMR). However, many people do not have access to IC devices and use prediction equations for RMR estimation. Equations using fat free mass (FFM) as a predictor have been developed to estimate RMR, as a strong relationship exists between FFM and RMR. One such equation is the Nelson equation which is used by the BodPod (BP). Yet, there is limited evidence whether the Nelson equation is superior to other common equations to predict RMR. To examine the agreement between predicted RMR from common RMR equations and the BP, and RMR measured via IC. Data from 48 healthy volunteers who completed both the BP and IC were collected. Agreement between RMR measured by BP, common regression equations, and indirect caloriometry was evaluated using repeated measures ANOVA, Bland-Altman analysis and root mean square error (RMSE). Predicted RMR values from common equations and BP were significantly different from IC with the exception of the World Health Organization (WHO) equation. Large limits of agreement and RMSE values demonstrate a large amount of error at the individual level. Despite the use of FFM, the Nelson equation does not appear to be superior to other common RMR equations. Although the WHO equation presented the best option within our sample, all equations performed poorly at the individual level. Clinicians should be aware that prediction equations may significantly under- or overestimate RMR compared to IC and when an accurate value of RMR is required, IC is recommended

Simulated Tibiofemoral Joint Reaction Forces for Three Previously Studied Gait Modifications in Healthy Controls

Gait modifications, such as lateral trunk lean (LTL), medial knee thrust (MKT), and toe-in gait (TIG), are frequently investigated interventions used to slow the progression of knee osteoarthritis. The Lerner knee model was developed to estimate the tibiofemoral joint reaction forces (JRF) in the medial and lateral compartments during gait. These models may be useful for estimating the effects on the JRF in the knee as a result of gait modifications. We hypothesized that all gait modifications would decrease the JRF compared to normal gait. Twenty healthy individuals volunteered for this study (26.7 ± 4.7 years, 1.75 ± 0.1 m, 73.4 ± 12.4 kg). Ten trials were collected for normal gait as well as for the three gait modifications: LTL, MKT, and TIG. The data were used to estimate the JRF in the first and second peaks for the medial and lateral compartments of the knee via opensim using the Lerner knee model. No significant difference from baseline was found for the first peak in the medial compartment. There was a decrease in JRF in the medial compartment during the loading phase of gait for TIG (6.6%) and LTL (4.9%) and an increasing JRF for MKT (2.6%). but none was statistically significant. A significant increase from baseline was found for TIG (5.8%) in the medial second peak. We found a large variation in individual responses to gait interventions, which may help explain the lack of statistically significant results. Possible factors influencing these wide ranges of responses to gait modifications include static alignment and the impacts of variation in muscle coordination strategies used, by participants, to implement gait modifications

Accuracy of 5 Common Age-Predicted Maximal Heart Rate Equations

International Journal of Exercise Science 13(7): 1242-1250, 2020. Age-predicted maximal heart rate (APMHR) is an essential measure for healthcare professionals in determining cardiovascular response to exercise testing, exertion, and prescription. Although multiple APMHR prediction equations have been validated for specific populations, the accuracy of each within a general population requires testing. We aimed to determine which APMHR equation (Fox, Gellish, Gulati, Tanaka, Arena, Astrand, Nes, Fairbarn) most accurately predicts max heart rate (HRmax) in a general population. HRmax from 99 graded treadmill exercise tests (GXT) were measured. GXTs ended upon volitional fatigue and were only included for analysis if RER \u3e 1.10. Individual paired t-test were performed to determine if significant differences existed between measured and predicted HRmax,along with root mean square errors for each equation. Bland-Altman plots were constructed to determine agreement between equations and measured HRmax. Significant differences between measured and predicted HRmax were found for the Gulati, Astrand, Nes, and Fairbarn (male) equations (p \u3c 0.05). Bland-Altman plots revealed wide limits of agreement for all nine APMHR equations, suggesting poor agreement between measured and predicted HRmax. Proportional bias indicates that prediction equations under and overestimated HRmax in individuals with lower and higher measured HRmax, respectively, with the exception of the Fox equation. All equations used in this study show poor agreement between measured HRmax and APMHR. The Fox equation may represent the best option for a general population as it is less likely to under or overestimate based on individual HRmax. Individuals should use data from GXTs to determine HRmax when applicable to ensure accuracy

Current Evidence of Gait Modification with Real-time Biofeedback to Alter Kinetic, Temporospatial, and Function-Related Outcomes: A Review

Background: Gait retraining using real-time biofeedback (RTB) may have positive outcomes in decreasing knee adduction moment (KAM) in healthy individuals and has shown equal likelihood in patients with knee osteoarthritis (OA). Currently, there is no consensus regarding the most effective gait modification strategy, mode of biofeedback or treatment dosage. Objective: The purpose of this review was: i) to assess if gait retraining interventions using RTB are valuable to reduce KAM, pain, and improve function in individuals with knee osteoarthritis, ii) to evaluate the effectiveness of different gait modifications and modes of RTB in reducing KAM in healthy individuals, and iii) to assess the impact of gait retraining interventions with RTB on other variables that may affect clinical outcomes. Methods: Seven electronic databases were searched using five search terms. Studies that utilized any form of gait retraining with RTB to improve one or a combination of the following measures were included: KAM, knee pain, and function. Twelve studies met the inclusion criteria, evaluating eleven distinctive gait modifications and three modes of RTB. Results: All but one study showed positive outcomes. Self-selected and multi-parameter gait modifications showed the greatest reductions in KAM with visual and haptic RTB being more effective than auditory. Conclusions: Current evidence suggests that gait modification using RTB can Positively alter KAM in asymptomatic and symptomatic participants. However, the existing literature is limited and of low quality, with the optimal combination strategies remaining unclear (gait and biofeedback mode). Future studies should employ randomized controlled study designs to compare the effects of different gait modification strategies and biofeedback modes on individuals with knee OA