Skeletal Muscle Desaturation during Moderate and Severe Intensity Cycling Exercise is related to Half-Time from the Reactive Hyperemia Response

Reactive hyperemia responses are a common method for assessing peripheral microvascular function, and the half-time reperfusion response, in particular, has been shown to be highly reproducible and sensitive to vascular dysfunction associated with disease. It is unclear, however, whether this parameter also provides information about microvascular responses during exercise. PURPOSE: The aim of this study was to determine the relationship between reactive hyperemia half-time response and the change in skeletal muscle oxygenation during cycling exercise. METHODS: Thirty young adults (20 men, 10 women; Age: 23 ± 5 y; peak VO2: 35.4 ± 4.4 ml/kg/min) completed a maximal cycling exercise test to determine moderate (95% gas exchange threshold, GET) and severe (75% of the difference between the GET and peak VO2) intensities for a cycling exercise protocol. Upon a follow-up visit, reactive hyperemia was measured using near-infrared spectroscopy (NIRS; PortaLite, Artinis Medical Systems, Netherlands) at the vastus lateralis muscle. Following a five-minute occlusion period, oxygen resaturation rate was recorded, and the half-time was determined as the time to reach 50% of the peak hyperemic response. During cycling exercise, the change in tissue saturation index (TSI) was measured using NIRS at the same location on the vastus lateralis as reactive hyperemia. Steady-state TSI was recorded as the 60-second average at the end of two 4-minute bouts of moderate exercise, and at 60-seconds into one bout of severe exercise. TSI is reported as the change in oxygenation from a 20 Watt cycling baseline. Pearson correlations were used to determine associations, and data are reported as mean ± SD. RESULTS: The half-time resaturation response was found to be 16.5 ± 4.9 seconds. The change in TSI during moderate exercise (-3.4 ± 2.4%) was correlated with the half-time response (r = -0.42, p = 0.01). Additionally, the change in TSI during severe exercise (-7.2 ± 2.8%) was correlated with the half-time response (r = -0.53, p = 0.002). These results remained significant after adjusting for sex or skinfold thickness using partial correlation. CONCLUSION: Our results suggest that lower peripheral microvascular vasodilatory capacity at rest, as indicated by the TSI half-time response, is related to higher rates of desaturation during moderate and severe intensity cycling exercise

Validity of Infrared 3-dimensional Scanning for Estimation of Body Composition: A 4-Compartment Model Comparison

Multiple infrared 3-dimensional (3D) scanning technologies exist, including time of flight (ToF) scanners and structured light scanners with static (SL-S) and dynamic (SL-D) configurations. ToF scanners measure depth by using the round-trip time of reflected photons, whereas SL scanners measure deformations in light patterns and allow for creation of a depth image using geometric triangulation. Recently, 3D scanning technologies have been proposed as novel methods of body composition assessment. PURPOSE: The purpose of this analysis was to examine the validity of four different commercially-available 3D scanners for estimation of body fat percentage (BF%) as compared to a 4-compartment (4C) model criterion. METHODS: After an overnight fast, 101 adults (63 F, 38 M; age: 29.3 ± 13.5 y; BMI: 24.3 ± 3.9 kg/m2; BF%: 24.6 ± 8.3%) completed assessments via dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), bioimpedance spectroscopy (BIS), a standard body mass scale, and four infrared 3D scanners. Two scanners (3DSSL-D1; 3DSSL-D2) utilized structured light scanning with a dynamic configuration, one utilized structured light scanning with a static configuration (3DSSL-S), and one utilized time-of-flight technology (3DSToF). Using the equation of Wang et al. (2002), a criterion 4C estimate of BF% was obtained using DXA for bone mineral, ADP for body volume, scale for body mass, and BIS for total body water. BF% estimates were compared using one-way ANOVA with Bonferroni adjustment for multiple comparisons, and additional evaluations were conducted using the correlation coefficient (r), constant error (CE), standard error of the estimate (SEE), total error (TE), and 95% limits of agreement (LOA). RESULTS: Estimates of BF% did not significantly differ between 4C and any of the 3D scanners. However, metrics of group, individual, and prediction errors varied between scanners: 3DSSL-D1: p=1.0; CE: 0.4%; r: 0.91; SEE: 2.5%; TE: 3.6%; LOA: ±7.0%; 3DSSL-D2: p= 1.0; CE: 0.8%; r: 0.86; SEE: 4.2%; TE: 4.7%; LOA: ±9.2%; 3DSSL-S: p= 1.0; CE: 1.0%; r: 0.81; SEE: 4.0%; TE: 5.0%; LOA: ±9.7%; 3DSToF: p=0.08; CE: -2.9%; r: 0.86, SEE: 2.5%; TE: 5.2%; LOA: ±8.6%. CONCLUSION: All three structured light scanners exhibited low magnitudes of group error (CE ≤ 1%) and may be valid assessment methods when analyzing the body composition of groups. 3DSSL-D1 exhibited the lowest group-level error (i.e. CE), prediction errors (i.e. SEE; TE), and individual error (i.e. LOA) of all scanners. Therefore, this device was deemed the most valid 3D scanner for body composition assessment. 3DSSL-D2, 3DSSL-S, and 3DSToF exhibited comparable TE, although group-level error was lower in 3DSSL-D2 and 3DSSL-S, while the SEE and individual-level error was lower for 3DSToF. However, individual-level errors were relatively high with all scanners (LOA ≥ 7%), which calls into question the utility of these methods for assessing the body composition of individuals. Nonetheless, additional research is needed regarding the ability of 3DS to successfully detect changes in body composition over time

Validity of Four-Compartment Model Body Fat Using Single- or Multi-frequency Bioelectrical Impedance Analysis to Estimate Body Water

Most common body composition assessment techniques make assumptions about the body, including the density and hydration of fat-free mass (FFM). An advantage of the four-compartment (4C) model is the ability to take these FFM characteristics into account when assessing body composition, thus reducing potential error. The total body water (TBW) estimate utilized in 4C models is particularly important due to the large contribution of water to an adult human’s total body mass (~40 - 70%) and FFM (~68 - 81%); however, the impact of utilizing different estimates of TBW within 4C model has not been fully explored. PURPOSE: The purpose of this investigation was to examine the validity of body fat percentage (BF%) estimates produced by 4C models utilizing single- or multi-frequency bioelectrical impedance analysis (BIA) TBW estimates as compared to a criterion 4C with TBW from bioimpedance spectroscopy (BIS). METHODS: After an overnight food and fluid fast, a sample of 101 adults (63 F, 38 M; age: 29.3 ± 13.5 y; BMI: 24.3 ± 4.0 kg/m2; BF%: 24.5 ± 8.3%) completed assessments via dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), BIS, single-frequency BIA (SFBIA), multi-frequency BIA (MFBIA) and a body mass scale. A criterion 4C model (4CBIS) estimate of BF% was obtained using DXA for bone mineral, ADP for body volume, scale for body mass, and BIS for TBW. BIS was used as the reference TBW method due to its more direct estimation of TBW via mathematical procedures (i.e. Cole modeling and mixture theories) as compared to the prediction equations used by BIA. Alternate 4C estimates of BF% were produced using TBW values from MFBIA (4CMFBIA) and SFBIA (4CSFBIA). BF% estimates were compared using one-way ANOVA, and additional evaluations were conducted using the coefficient of determination (R2), constant error (CE), total error (TE), and 95% limits of agreement (LOA). RESULTS: BF% did not differ between 4CBIS (24.5 ± 8.3%), 4CMFBIA (24.4 ± 8.9%), and 4CSFBIA (25.7 ± 8.3%; p=0.52). 4CMFBIA exhibited negligible CE (-0.1 ± 2.3%), R2 of 0.97, TE of 2.3%, and LOA of 4.4%. 4CSFBIA exhibited a small CE (1.2 ± 1.2%), R2 of 0.98, TE of 1.6%, and LOA of 2.3%. CONCLUSION: At the group level, BF% estimates did not differ between any 4C model, indicating that both SFBIA and MFBIA can serve as viable alternatives to BIS for TBW estimation. Although the magnitude of group error (i.e. CE) was slightly smaller in 4CMFBIA, the individual error (i.e. LOA) and total error were smaller in 4CSFBIA,indicating that SFBIA TBW estimates may be more appropriate when tracking body composition changes within individuals using a 4C model. While the MFBIA and SFBIA technologies employed in the present study exhibited good validity, these results may not be attributable to all BIA analyzers. The quality of assessment device, affordability, portability and ease of use should be considered when utilizing an impedance-based technology for TBW estimation in a 4C model

Day-to-Day Precision Error and Least Significant Change for Two Commonly Used Bioelectrical Impedance Analysis Devices

Bioelectrical impedance analysis (BIA) devices administer electrical currents through surface electrodes to estimate overall body fluids from the measured resistance and reactance of bodily tissues. The proportion of fat versus fat-free mass can be further estimated by these devices using algorithms developed from reference data. BIA devices are commonly used in field as well as laboratory settings due to their convenience, ease of use, and relatively low cost. PURPOSE: The purpose of this study was to determine the day-to-day precision error (PE) and least significant change (LSC) of the percent body fat (PBF), fat mass (FM), and fat-free mass (FFM) estimated by two commonly used BIA devices, the InBody 770 and the Omron HBF-306. METHODS: Seventeen healthy participants (7 males, 10 females) were included in this analysis. Participants visited the laboratory on two separate occasions no more than 48 hours apart and abstained from all food, fluid, caffeine, and alcohol for at least 8 hours prior to each visit. Height and weight were measured using a Seca 769 stadiometer and digital scale. PE was calculated as , where SD is the within-subject standard deviation. LSC was calculated as 2.77 * PE to reflect a 95% confidence level. RESULTS: Participants had a mean ±SD age of 27.1 ±8.3 years, height of 171.6 ±8.5 cm, and weight of 68.0 ±10.6 kg. PE for the InBody was 1.0%, 0.7 kg, and 0.9 kg for PBF, FM, and FFM, respectively; PE for the Omron was 0.6%, 0.4 kg, and 0.6 kg for the same variables. The LSC values of each variable for the InBody were 2.8%, 1.9 kg, and 2.4 kg for PBF, FM, and FFM, respectively; the LSC values for these variables were 1.5%, 1.0 kg, and 1.6 kg for the Omron device. CONCLUSION: Individuals looking to use BIA as a method of detecting true changes in body composition over time should be aware that day-to-day measurement error between estimates were as as high as 1.0% for body fat, 0.7 kg for fat mass, and 0.9 kg for fat-free mass in the current study; therefore, changes within these parameters likely reflect error of measurement and not true physiological differences. Additionally, changes over time between estimates from an InBody 770 device should meet or exceed a difference of at least 2.8% body fat, 1.9 kg FM, or 2.4 kg FFM to increase confidence that the differences are a reflection of physiological changes rather than between-day measurement error; differences between readings from an Omron should meet or exceed 1.5% body fat, 1.0 kg FM, or 1.6 kg FFM for this purpose. The InBody 770 demonstrated higher precision error and thus may entail a higher least significant change to meaningfully detect true physiological changes between time points. However, the observed differences in these values between the InBody 770 and Omron HBF-306 may also indicate that the InBody 770 is more sensitive to small but real changes in bioelectrical impedance values between days. Longitudinal studies are needed to elucidate the comparative tracking validity of these commonly used BIA devices in healthy populations

Analyzing the Between-Day Reliability of Three-Dimensional Body Scanners for Body Composition Assessment

In the growing world of health and well-being, three-dimensional (3D) scanning is emerging as a popular tool to assess body composition. While body composition cannot be truly “measured” in living humans, it can be approximated. This, however, leads to two types of errors (i.e., technical and biological) in the body composition assessment. PURPOSE: This study was used to determine what percent or range of percentages in body fat one must exceed before concluding a real change has occurred. By conducting assessments on separate days, with only a short period of time between them, true changes in body composition are unlikely to occur. Therefore, this design can help determine the inherent, between-day error in a body fat assessment to provide context for longer-term changes. METHODS: In the present investigation, thirteen participants were scanned using three distinct 3D body scanners (Fit3D Proscanner, Sizestream SS20, and Styku S100) on two separate mornings, separated by 24 to 48 hours. Each subject had to follow the pre-assessment restrictions and ensure they fit all the eligibility requirements for this study. Then, all body fat percentage (BF%) values from the 3D scanners were recorded and analyzed to determine the between-day reliability. RESULTS: Intraclass correlation coefficients ranged from 0.971 to 0.997 for the three scanners. The least significant change (LSC) values were 1.2%, 2.6%, and 3.0% for the Styku S100, Fit3D Proscanner, and Sizestream SS20, respectively. When examining differences in BF% for individual participants, the between-day differences ranged from -1.1% to 1.0% for Styku S100, -1.9% to 3.2% for Fit3D Proscanner, and -4.0% to 3.0% for SizeStream SS20. CONCLUSION: These results collectively suggest that the Styku S100 has the highest between-day reliability and lowest technical error of the three scanning systems. Overall, however, it is important for consumers to understand that each 3D scanner contains some level of error that should be considered when interpreting the results of an assessment. This study can not only be applied to future research determining the most reliable body composition assessments, but it can also aid individuals in understanding how large of a change in body fat is needed to exceed the error of a 3D scanner and therefore be considered a “real” change

Cross-sectional and Longitudinal Relationships Between Skinfold Thicknesses Obtained by Ultrasonography and Body Fat Estimates Produced by Dual-energy X-ray Absorptiometry

Ultrasonography (US) and dual-energy X-ray absorptiometry (DXA) are frequently used to assess body composition. Although sometimes compared cross-sectionally, their agreement longitudinally requires further exploration. Purpose: The purpose of the present study was to examine the cross-sectional and longitudinal relationships between total and segmental raw skinfold thicknesses obtained by US and total and segmental body composition estimates produced by DXA over the course of an overfeeding study. Methods: Twenty healthy, resistance-trained males (mean ± SD; age: 22.0 ± 2.6 years; height: 179.1 ± 7.0 cm; body mass: 74.8 ± 11.5 kg, body fat: 17.5 ± 4.5%) completed a 6-week intervention that included 3 weekly sessions of supervised resistance training (RT) and the consumption of a hypercaloric diet. Before and after the 6-week intervention, body composition was assessed using DXA and B-mode US on seven measurement locations specified by Jackson and Pollock. Relationships between DXA and US variables were examined using Pearson\u27s product-moment correlation (r) and Lin’s concordance correlation coefficient (CCC). Additional validity metrics were also calculated. Results: Cross-sectionally, correlations were observed between whole body DXA fat mass (FM) and total subcutaneous tissue thickness (r = 0.88 [95% CI: 0.72, 0.95]). Longitudinally, a significant correlation was observed between total DXA FM changes and total subcutaneous thickness changes (r = 0.49, CCC = 0.38). Correlations of similar magnitudes were observed for the upper body and trunk. In contrast, DXA FM changes were unrelated to changes in subcutaneous tissue thicknesses for the lower body and arms. Cross-sectionally, 2-compartment (2C) FM estimates from US and DXA FM were correlated (r = 0.91, CCC = 0.83). However, the mean difference between these FM estimates was 2.2 ± 2.1 kg (mean ± SD), and the total error (TE) between DXA and US FM estimates was 2.97 kg. Longitudinally, a weaker correlation was observed than that cross-sectionally (r = 0.47, CCC = 0.33), and the TE between DXA and US FM changes was 1.80 kg. Conclusion: Results from this study showed generally good agreement between DXA and US cross-sectionally, but a much weaker relationship longitudinally. In addition, DXA FM changes were unrelated to changes in subcutaneous tissue thicknesses for the lower body and arms, indicating better agreement when examining the upper body as compared to the lower body. Future research with US or calipers should report raw skinfold thicknesses, and the differences between common body composition estimation techniques should be considered when examining longitudinal body fat changes

Influence of Acute Water Ingestion on Bioelectrical Impedance Analysis Estimates of Body Composition

Body composition estimation is a significant component of health and fitness assessments. Multi-frequency bioelectrical impedance analysis (MFBIA) uses multiple electrical frequencies that travel through body tissues in order to estimate fluid content and body composition. Prior to body composition assessments, it is common to implement a wet fast (i.e., a fasting period that allows water intake); however, the influence of a wet fast as compared to a dry fast (i.e., disallowing water intake) is relatively unknown. PURPOSE: To determine the effects of acute water consumption on MFBIA body composition estimates. METHODS: A randomized crossover study was conducted in 16 adults (8 F, 8 M; age: 22.0 ± 2.9 y; height: 173.6 ± 9.9 cm; weight: 74.3 ± 21.6 kg; body mass index: 24.6 ± 4.7; body fat % [BF%]: 16.7 ± 8.1%). On two occasions, participants reported to the laboratory after an overnight food and fluid fast. After a baseline MFBIA assessment, participants either consumed 11 mL/kg of bottled water (W condition) or consumed no fluid as the control (CON condition). The 11 ml/kg dose of water corresponded to absolute intakes of 531 to 1360 mL. After the water consumption time point, MFBIA tests were performed every 10 minutes for one hour. Participants stood upright for the entire research visit. MFBIA estimates of body mass (BM), fat mass (FM), fat-free mass (FFM), and BF% were analyzed using 2 x 7 (condition x time) analysis of variance with repeated measures, follow-up pairwise comparisons, and evaluation of the partial eta-squared (ηp2) effect sizes. RESULTS: No variables differed between conditions at baseline. Condition x time interactions were present for all variables (BM: pp2=0.89; FM: p=0.0008, ηp2=0.30; BF%: p=0.005, ηp2=0.23) except FFM (p=0.69, ηp2=0.03). Follow-up testing indicated that BM was ~0.6 kg higher in W as compared to CON at all post-baseline time points (pp2=0.32), regardless of condition. CONCLUSION: Up to one hour after ingestion, acute water intake was exclusively detected as increased FM by MFBIA. This contrasts with the common belief that ingesting water prior to bioimpedance tests would result in inflated FFM and decreased BF%. Since body composition estimates never returned to baseline within the hour after water ingestion, it is not clear how long this effect would persist. These results suggest acute water ingestion can produce an inflation of MFBIA body fat estimates for at least one hour. These results indicate that water intake during fasting periods should be considered as part of pre-assessment standardization

Tracking Resistance Training-Induced Changes in Body Composition via 3-Dimensional Optical Scanning

Tracking changes in body composition is potentially useful for monitoring health status, disease risk, and results of lifestyle interventions. In active individuals, evaluating body composition changes over time may provide useful information regarding the effectiveness of nutrition and exercise programs. PURPOSE: The purpose of this study was to compare changes in body composition estimates obtained from a 4-compartment (4C) model and a 3-dimensional optical (3DO) scanner in resistance-trained males. METHODS: Twenty resistance-trained males underwent assessments via 4C and 3DO before and after 6 weeks of supervised resistance training plus overfeeding with a high-calorie protein/carbohydrate supplement. To generate the 4C model, tests were performed using dual-energy x-ray absorptiometry, air displacement plethysmography, and bioimpedance spectroscopy. Changes in fat mass (ΔFM) and fat-free mass (ΔFFM) detected by 3DO were compared with the reference 4C model using paired-samples t-tests, Bland-Altman analysis, equivalence testing, and evaluation of validity metrics. RESULTS: Both ΔFM (mean ± SD: 4C: 0.6 ± 1.1 kg; 3DO: 1.9 ± 1.9 kg) and ΔFFM (4C: 3.2 ± 1.7 kg; 3DO: 1.9 ± 1.4 kg) differed between methods (p \u3c 0.002). The correlation (r) for ΔFM was 0.49 (95% confidence interval [CI]: 0.06 to 0.77) and was 0.42 (95% CI: -0.03 to 0.73) for ΔFFM. The total error for ΔFM and ΔFFM estimates was 2.1 kg. ΔFFM demonstrated equivalence between methods based on a ± 2 kg (~62% of 4C change) equivalence interval, whereas ΔFM failed to exhibit equivalence even with a 100% equivalence interval. Proportional bias was observed for ΔFM but not ΔFFM. CONCLUSION: Our data indicate that changes in FM and FFM detected by a 3D scanner did not exhibit strong agreement with changes detected by a 4C model. However, within the context of our study, agreement in FFM changes was superior to agreement in FM changes based on the results of equivalence testing and lack of proportional bias in FFM changes. Therefore, depending on the level of accuracy needed, the error in FFM changes observed for the 3D scanner may be potentially acceptable for some applications. Future research should investigate the utility of 3D scanners for monitoring changes in body composition and anthropometric variables in healthy and clinical populations, as well as investigate novel body phenotypes that may be associated with disease risk or health status

Influence of Subject Presentation on Body Composition Estimates from Dual-Energy X-Ray Absorptiometry, Air Displacement Plethysmography, and Bioelectrical Impedance Analysis

Body composition assessment devices are commonly employed to track changes associated with exercise or nutritional interventions. However, many individuals undergo body composition assessments with little to no pre-testing standardization of dietary intake or physical activity, potentially introducing error into their results. PURPOSE: To examine the validity of unstandardized body composition assessments relative to standardized assessments using three common body composition assessment devices. METHODS: Twenty-three resistance-trained males (Mean ± SD; 21.6 ± 2.6 years; 71.3 ± 6.8 kg; 177.4 ± 5.9 cm; 17.4 ± 4.1% DXA-derived percent body fat [%BF]) underwent paired body composition assessments via dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), and single-frequency bioelectrical impedance analysis (BIA). Each participant’s initial standardized body composition assessments were performed in the morning following an overnight food and fluid fast and 12 hours of exercise and caffeine abstention, and all unstandardized assessments were performed later during the same day following ad libitum daily activities. Unstandardized estimates of %BF and fat-free mass (FFM) for each device were compared with device-specific standardized values using paired-samples t-tests, line of identity analysis, evaluation of validity metrics, Bland-Altman analysis, and equivalence testing. RESULTS: The total error between standardized and unstandardized %BF estimates was 0.66% for DXA [95% confidence interval {CI}: 0.56-0.76%], 1.60% for ADP [95% CI: 1.50-1.70%], and 1.85% for BIA [95% CI: 1.75-1.95%]. The total error for FFM estimates was 0.75kg for DXA [95% CI: 0.65-0.85kg], 1.15kg for ADP [95% CI: 1.06-1.25kg], and 1.68 kg for BIA [95%CI: 1.58-1.78]. %BF estimates did not differ between paired measurements for DXA (p = 0.17) or ADP (p = 0.10) but differed between BIA (p \u3c 0.001) assessments. Similarly, FFM estimates did not differ between paired measurements for DXA (p = 0.40) or ADP (p = 0.78) but differed between BIA assessments (p \u3c 0.001). All paired assessments for each outcome produced regression line slopes which differed from the line of identity (p \u3c 0.001). Only BIA %BF estimates exhibited an intercept that differed from the line of identity (p \u3c 0.001). No proportional bias was detected for any outcome. Equivalence was demonstrated between %BF estimates for DXA but not ADP or BIA, based on a ±1%BF equivalence interval. Equivalence was demonstrated for all FFM estimates except BIA, based on a ±1kg equivalence interval. CONCLUSION: Our findings suggest that DXA body composition estimates are more robust when conducted in an unstandardized state relative to ADP or BIA. These results can inform the choice of body composition assessment methodology when pre-testing standardization is not possible

Comparison of Indirect Calorimetry and Common Prediction Equations for Evaluating Changes in Resting Metabolic Rate Induced by Resistance Training and a Hypercaloric Diet

An individual’s resting metabolic rate (RMR) is commonly the largest contributor to total daily energy expenditure. Prediction equations are most often employed by practitioners to estimate RMR, due to their superior practicality in many settings relative to laboratory methods like indirect calorimetry (IC). The ability to quantify RMR change over time may be more valuable than cross-sectional estimates as practitioners can then utilize these changes to prescribe adjustments to one’s nutritional intake. PURPOSE: The purpose of this study was to assess the validity of several commonly used prediction equations to track RMR changes during a hypercaloric nutrition intervention and supervised exercise training program. METHODS: Twenty generally healthy males (mean ± standard deviation; age: 21.9 ± 2.6 years; height: 178.1 ± 6.9 cm; body mass: 72.2 ± 7.3 kg; fat-free mass index: 18.9 ± 1.5 kg/m2 ; bench press strength: 1.3 ± 0.2 kg/kg BM; leg press strength: 3.4 ± 0.9 kg/kg BM) completed a supervised resistance training program in conjunction with a hypercaloric diet. The protocol lasted 6 weeks, and participants completed RMR assessments via IC pre-and post-intervention to obtain reference values. Existing RMR prediction equations based on body mass or fat-free mass were also evaluated. Equivalence testing was used to evaluate whether each prediction equation demonstrated equivalence with IC based on a ± 50 kcal/d equivalence region, and the confidence limits for the two-one-sided t-tests were calculated. Null hypothesis significance testing was performed, and Bland-Altman analyses were utilized alongside linear regression to assess the degree of proportional bias. RESULTS: IC RMR values increased by 165 ± 97 kcal/d. All prediction equations underestimated RMR changes, relative to IC, with magnitudes ranging from 75 to 132 kcal/d, while also displaying unacceptable levels of negative proportional bias. Additionally, all prediction equations significantly differed from measured IC values, and no equation demonstrated equivalence with IC. CONCLUSION: These findings suggest the examined prediction equations are not acceptable for tracking RMR changes in resistance-trained males, within the context of the present study. The consistent underestimation of RMR changes indicates that the input variables, and their weights within the prediction equations, were insufficient to adequately explain the observed changes in RMR