Measurement of advection and surface-atmosphere exchange in complex terrain.

grant NER/S/J/2004/13118Accurate observations of the carbon cycle are essential as inputs to global climate models. Observations made by the micrometeorological technique of eddy covariance, whist widespread, may be incorrect if air is advected away from below the sensor system. This is potentially a serious issue for FLUXNET, a global network of eddy flux sites. The approach in this thesis to investigate this problem was twofold: A full micrometeorological mass balance using an instrumented 50 m long by 50 m wide by 6 m high Cartesian control volume (CV) covering the understorey vegetation of a 40 m high Eucalyptus forest was carried out; situated adjacent to the Tumbaruma eddy covariance site in Australia. At night positive (into the atmosphere) advection fluxes caused by down-slope katabatic drainage within the forest trunk space, dominated the CO2 flux budget of the CV, with both vertical and horizontal advection terms having predominantly positive values. The nighttime estimates of advection were subject to large systematic errors that were of the same order of magnitude as the advection signal. Nevertheless, the nocturnal respiration flux of the understorey vegetation was clearly resolved by the diurnal full mass balance flux curve that resulted from the experiment, having a typical value of 5 μmol m-2 s-1. A second experiment carried out at the Griffin forest in Scotland demonstrated the presence of sub-canopy katabatic/gravity flows at night that would be likely to cause scalar advection resulting in underestimation of the nocturnal respiration flux of CO2. Finally, it is recommended that the micrometeorological mass balance technique should not be deployed across FLUXNET because of financial cost and issues of systematic error

Relationship Between Changes in Upper Body Fat-Free Mass and Bench Press Performance in American Football Players

Horizontal pushing strength and strength endurance are relevant attributes for blocking and block shedding in American football. Since most positions in American football require the ability to either block or shed a block, and since bench press repetitions to failure (RTF) with 225 pounds is a component of the NFL draft combine, improving horizontal pushing strength and strength endurance have been key areas of emphasis for strength and conditioning coaches working with these athletes. PURPOSE: The purpose of this analysis was to quantify the relationship between changes in upper body fat-free mass (FFM) and metrics of bench press performance in American football players. METHODS: Body composition and muscular performance were assessed in NCAA Division III American football players. Upper body FFM was obtained from dual-energy X-ray absorptiometry (DXA; Hologic Horizon) before and after a seven-week offseason training period. Barbell bench press one-repetition maximum (1RM), incline barbell bench press 1RM, and RTF with 225 pounds on the barbell bench press were also determined before and after the training period. Using Spearman’s rank correlations, the relationships between percent changes in upper body FFM and bench press 1RM (n=19), bench press RTF with 225 pounds (n=15), and incline bench press 1RM (n=18) were evaluated. RESULTS: Relative changes in bench press 1RM and DXA upper body FFM exhibited a weak, non-significant correlation (ρ: 0.38, p: 0.11). However, there was a moderate strength, significant correlation between relative changes in bench press RTF with 225 pounds and DXA upper body FFM (ρ: 0.53, p: 0.04). For relative changes in incline bench press 1RM, there was a weak, non-significant correlation with DXA upper body FFM (ρ: 0.24, p: 0.36). CONCLUSION: Of the performance tests assessed, only changes in bench press RTF with 225 pounds and changes in DXA upper body FFM were positively correlated. Therefore, strength and conditioning coaches working with athletes who plan on entering the NFL draft may want to consider dedicating time to increasing upper body FFM gains prior to the draft as it appears to be positively correlated with performance on this NFL combine test

Survivorship care for older adults with cancer: U13 conference report

Older adult cancer survivors currently account for almost 60% of all cancer survivors. The number of older cancer survivors will continue to increase as the population ages and as patients’ live longer after a cancer diagnosis. As part of cancer center accreditation, the American College of Surgeons Commission on Cancer® (CoC) has placed great importance on survivorship care planning. While the CoC has set standards for general survivorship care, there is sparse evidence on how to best care for older adult cancer survivors. Concern exists among the medical community that survivorship care plans could increase paperwork without improving outcomes. Given the diverse and unique needs of older adult cancer survivors, the inter-professional team provides a structure and process for survivorship care built around the particular needs of older adults. The Cancer and Aging Research Group (CARG), in partnership with the NIA/NCI, held a U13 conference in May 2015 in part to discuss survivorship care for older adults with cancer. This report discusses four themes that emerged from one section of the conference: (1) survivorship care is a process that continually evolves to meet the needs of older adults; (2) older adult cancer survivors have unique needs and care plans should be tailored to meet these needs; (3) the inter-professional team is ideally suited to structure survivorship care of older adults; (4) patient advocacy must be encouraged throughout the cancer care continuum. As evidence based survivorship practices develop, the unique needs of older adults need to be given substantial attention

The Effect of Body Composition Methodology on Resulting Energy Availability Assessments

Energy availability (EA) is defined as the total daily energy available to an individual after accounting for that expended during exercise and standardized to fat-free mass (FFM). Generally, EA values less than 30 kcal/kg FFM/day are considered “low” and have been associated with deleterious effects on reproductive and hormonal health in females. However, it is unclear whether the method used to estimate FFM influences the resulting EA values to a degree that may affect interpretation and clinical decision-making. PURPOSE: To determine the effect of FFM values derived from various methods of body composition assessment on the resulting range and interpretation of EA values. METHODS: Four EA estimates were generated in 38 healthy females (mean ± SD age: 25.6 ± 6.2 years; height: 163.6 ± 7.4 cm; weight: 64.7 ± 13.8 kg) using different combinations within a reasonable range of lower and higher (25 and 35 kcal/kg bodyweight, respectively) energy intake values and lower and higher (3.5 and 7 kcal/kg bodyweight, respectively) exercise energy expenditure values. Resulting estimates were then standardized to FFM values from air displacement plethysmography (ADP), bioelectrical impedance spectroscopy (BIS), and bioelectrical impedance analysis (BIA) from both a research-grade (multi-frequency) and consumer-grade (dual-frequency) device. Resulting EA values were then compared to those using FFM from dual-energy x-ray absorptiometry (DXA). Each estimate was assigned to one of three EA “zones”: “low” (less than 30 kcal/kg FFM), “reduced” (30-44.9 kcal/kg FFM), or “adequate” (≥45 kcal/kg FFM). Individual EA estimates that were in different zones when compared between two devices were considered discordant. RESULTS: When compared to DXA-derived estimates, EA values were discordant in up to 13-16% of individuals depending on body composition method used. Discordant values were generally more common in the plots assuming higher (35 kcal/kg bodyweight) energy intake values and were most likely to be considered “adequate” using DXA-derived FFM versus “reduced” using alternate methods. CONCLUSION: EA estimates are generally robust to the method of body composition assessment used. However, divergent interpretations may occur in a small minority of individuals in which alternate methods may provide lower EA values than DXA

A Between-sex Comparison of the Validity of Body Fat Percentage Estimates From Four Bioelectrical Impedance Analyzers

Bioelectrical impedance analysis (BIA) devices administer electrical currents through surface electrodes in contact with the hands and/or feet. The measured reactance and resistance of various bodily tissues to these currents are then used to estimate body fat percentage (BFP) and other body composition values of interest based on algorithms derived from validation data. Owing to different patterns of fat distribution between sexes, it is unclear whether the configuration of electrodes (i.e., hand-to-hand, foot-to-foot, or hand-to-foot) may affect the validity of these devices in males versus females. PURPOSE: The purpose of this study was to determine the validity of BFP values across four BIA devices – one consumer-grade foot-to-foot device (RENPHO Smart Bathroom Scale), one consumer-grade hand-to-hand device (Omron HBF-306), one consumer-grade octapolar device (InBody H20N), and one research-grade octapolar device (Seca mBCA 515/514) – against a criterion four-compartment model (4C), and to compare these values between males and females. METHODS: Seventy-four healthy participants (35 males and 39 females) were included in this analysis. Participants abstained from all food, fluid, caffeine, and alcohol for at least 8 hours prior to each visit. Total error (TE) was calculated as the root mean square error between the estimate of each BIA device and that of the 4C model. Standard error of the estimate (SEE) was defined as the residual standard error value from ordinary least squares regression. Constant error (CE) was calculated as the average difference between the estimate of each BIA device and that of the 4C model. RESULTS: Participants had a mean ±SD age of 27.2 ±7.3 years, height of 168.1 ±8.9 cm, weight of 72.2 ±16.7 kg, and 4C BFP of 24.9 ±9.2%. In the entire sample, ranges for validity metrics of interest were as follows: TE: 3.2% (Seca) to 7.2% (RENPHO); SEE: 3.3% (Seca) to 5.7% (RENPHO); CE: -0.02 ±3.4% (InBody) to -3.46 ±4.1% (Omron). Across all devices, both TE and SEE were lower in females, with the largest between-sex differences observed for the InBody and RENPHO. Both octapolar devices (InBody and Seca) exhibited low group-level error in males and females (all CE within ±0.32%). Meanwhile, the RENPHO and Omron devices generally underestimated BFP with a greater degree of underestimation in females (CE of -2.6% and -3.7%, respectively) than males (CE of -0.1% and -3.2%, respectively), particularly for the RENPHO. CONCLUSION: Among the four BIA devices investigated, octapolar devices tended to have higher validity overall. All devices demonstrated lower TE and SEE in females, with the greatest between-sex differences observed in the InBody and RENPHO models. Users should be aware that commercially available hand-to-hand or foot-to-foot BIA devices such as the Omron and RENPHO models used in this study may systematically underestimate BFP compared to a criterion 4C model. In contrast, hand-to-foot octapolar analyzers exhibit strong group-level validity in both sexes

Comparison of Laboratory-Grade and Consumer-Grade Hand-to-Foot Bioelectrical Impedance Analyzers for Body Composition Estimation

Bioelectrical impedance analysis (BIA) is a simple and effective technique to estimate body composition, including body fat percentage (BFP). While these analyzers are a popular method of describing a person’s body composition, laboratory-grade devices are expensive and inaccessible to most people. As a result, they may be an unrealistic method for consumers to use. However, consumer-grade devices are increasingly available. PURPOSE: The purpose of this study was to compare laboratory-grade and consumer-grade bioelectrical impedance analyzers. METHODS: Seventy-five adults (40 F, 35 M) were evaluated using a laboratory-grade, hand-to-foot, multifrequency bioelectrical impedance analyzer (BIALAB; Seca mBCA 515) and a consumer-grade, hand-to-foot, single frequency bioelectrical impedance analyzer (BIACON; Omron HBF-516). Both devices administer undetectable electrical pulses through one extremity that are measured at another extremity, where the voltage drop (impedance) is determined. This information is used to estimate body fluids and composition. RESULTS: A strong, statistically significant correlation between devices was observed for BFP (r: 0.93, R2: 0.87, pCON overestimated BFP by 3.5 ± 3.4% (mean ± SD) relative to BIALAB (BIACON: 28.3 ± 9.6%; BIALAB: 24.8 ± 9.3%; pCONCLUSION: These results collectively suggest that while the laboratory-grade and consumer-grade analyzers in our study exhibit strong correlations when assessing a group of individuals, the consumer-grade device overestimates BFP. Additionally, the SEE indicates that 3.4% error can be expected with the consumer-grade device. Overall, the Omron HBF-516 consumer-grade device may be an adequate and affordable option to estimate body composition in some contexts, but results should be interpreted cautiously when used in individuals

Validity of Hand-to-Foot and Foot-to-Foot Consumer Bioimpedance Analyzers: A Four-Compartment Model Comparison

Body fat percentage (BF%) is a useful variable for predicting disease risk and determining overall fitness. Consumer-grade bioimpedance analyzers seek to provide accurate body composition data while remaining affordable and accessible. PURPOSE: The purpose of this study was to compare body fat percentages obtained from hand-to-foot and foot-to-foot consumer bioimpedance analyzers to a gold standard 4-compartment (4C) model. METHODS: Seventy-five adults (40 F, 35 M; age: 27.2 ± 7.3 y; height: 168.1 ± 8.8 cm; BM: 72.1 ± 16.6 kg; 4C model BF%: 25.0 ± 9.2%) were evaluated by a 4C model, a consumer-grade hand-to-foot bioimpedance analyzer (BIA-HF; Tanita BC568) and two consumer-grade foot-to-foot bioimpedance analyzers (BIA-FF; Tanita BC554 and Tanita UM081). The 4C model comprised dual-energy X-ray absorptiometry, air displacement plethysmography, and bioimpedance spectroscopy. BF% estimates obtained by each bioimpedance analyzer were compared to the criterion 4C using the coefficient of determination (R2), standard error of the estimate (SEE), and Bland-Altman analysis. RESULTS: BIA-HF underestimated BF% by 1.4 ± 4.1%, and both BIA-FF overestimated BF% by 0.5 to 0.6 ± 5.7%. The R2 value was higher for BIA-HF as compared to both BIA-FF analyzers (0.81 vs. 0.64). The SEE and 95% limits of agreement (LOA) were lower for BIA-HF (SEE: 4.0%; LOA: 8.1%) as compared to both BIA-FF (SEE: 5.6%; LOA: 11.2%). No method demonstrated proportional bias based on Bland-Altman analysis. CONCLUSION: While both hand-to-foot and foot-to-foot consumer-grade bioimpedance analyzers demonstrated potentially meaningful errors when compared to a gold standard method, the hand-to-foot device exhibited better overall performance. Specifically, a stronger linear agreement with the 4C model and lower individual-level errors were observed with the hand-to-foot model as compared to both foot-to-foot models from the same manufacturer. The superior performance of the hand-to-foot analyzer could be due to its direct testing of both the upper and lower body, which is more similar to the methods used in the 4C model and a better representation of an individual’s overall body composition

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

Body Composition Estimation in Youth Athletes: Agreement Between Two-Component Methods

Body composition techniques such as skinfold measurements, air displacement plethysmography, and underwater weighing are commonly performed in athletic populations, particularly in youth athletes who may not have access to other laboratory methods. However, little is known whether such body composition estimates can be directly compared across techniques. PURPOSE: To determine the agreement between common two-component (2C) body composition techniques. METHODS: 90 youth athletes (Males: 39; Females: 51; Age: 18.2 ± 2.4 years; Height: 172.0 ± 9.9 cm; Body Mass: 69.0 ± 12.5 kg; Underwater Weighing [UWW] Body Fat Percentage [%BF]: 20.2 ± 7.0%) participated in this study. 2C estimates of %BF were determined via UWW, air displacement plethysmography (ADP), and 7-site skinfold (SKF) using the applicable Jackson-Pollock equation. Body mass was measured via calibrated scale. Agreement between methods was quantified using Lin’s concordance correlation coefficients (CCC). Estimates of body fat percentage were also compared between techniques using paired samples t-tests (α \u3c 0.05) and equivalence testing, with the threshold of equivalence set at ± 2% body fat. RESULTS: Mean ± SD %BF estimates were 20.2 ± 7.0% (UWW), 18.7 ± 7.3% (ADP), and 16.1 ± 7.2% (SKF). Mean differences between methods were 1.6% [95% CI: 0.8, 2.3] for UWW vs. ADP, 4.1% [95% CI: 3.4, 4.8] for UWW vs. SKF, and 2.6% [95% CI: 1.9, 3.2] for ADP vs. SKF. Paired-samples t-tests revealed significant differences between %BF estimates for each comparison. Likewise, no methods were found to be equivalent, based on a ± 2% BF equivalence range. CCC values were 0.855 for UWW vs. ADP, 0.759 for UWW vs. SKF, and 0.844 for ADP vs. SKF. CONCLUSION: This study suggests limited agreement between 2C %BF estimates derived from three common assessment techniques. Hypothesis testing revealed significant differences between methods, and the magnitude of these differences resulted in non-equivalence at ± 2% BF. Based on these results, it appears that direct comparisons between 2C %BF estimates from these different techniques should be avoided if possible. Though the magnitude of the differences between techniques may be acceptable in certain contexts, coaches and clinicians should strive to utilize the same assessment methodology when examining and comparing body composition results across time