Standard Error Of The Estimate Research Articles

Optimizing exercise prescription during breast cancer rehabilitation in women: Analysis of the load–velocity relationship in the box squat exercise

AbstractThe aims of this study were to assess (i) the load–velocity relationship during the box squat exercise in women survivors of breast cancer, (ii) which velocity variable (mean velocity [MV], mean propulsive velocity [MPV], or peak velocity [PV]) shows stronger relationship with the relative load (%1RM), and (iii) which regression model (linear [LA] or polynomic [PA]) provides a greater fit for predicting the velocities associated with each %1RM. Nineteen women survivors of breast cancer (age: 53.2 ± 6.9 years, weight: 70.9 ± 13.1 kg, and height: 163.5 ± 7.4 cm) completed an incremental load test up to one‐repetition maximum in the box squat exercise. The MV, MPV, and the PV were measured during the concentric phase of each repetition with a linear velocity transducer. These measurements were analyzed by regression models using LA and PA. Strong correlations of MV with %1RM (R2 = 0.903/0.904; the standard error of the estimate (SEE) = 0.05 m.s−1 by LA/PA) and MPV (R2 = 0.900; SEE = 0.06 m.s−1 by LA and PA) were observed. In contrast, PV showed a weaker association with %1RM (R2 = 0.704; SEE = 0.15 m.s−1 by LA and PA). The MV and MPV of 1RM was 0.22 ± 0.04 m·s−1, whereas the PV at 1RM was 0.63 ± 0.18 m.s−1. These findings suggest that the use of MV to prescribe relative loads during resistance training, as well as LA and PA regression models, accurately predicted velocities for each %1RM. Assessing and prescribing resistance exercises during breast cancer rehabilitation can be facilitated through the monitoring of movement velocity.

Determination of resistance at zero and infinite frequencies in bioimpedance spectroscopy for assessment of body composition in babies

Objective. Bioimpedance spectroscopy (BIS) is a popular technique for the assessment of body composition in children and adults but has not found extensive use in babies and infants. This due primarily to technical difficulties of measurement in these groups. Although improvements in data modelling have, in part, mitigated this issue, the problem continues to yield unacceptably high rates of poor quality data. This study investigated an alternative data modelling procedure obviating issues associated with BIS measurements in babies and infants. Approach. BIS data are conventionally analysed according to the Cole model describing the impedance response of body tissues to an applied AC current. This approach is susceptible to errors due to capacitive leakage errors of measurement at high frequency. The alternative is to model BIS data based on the resistance–frequency spectrum rather than the reactance-resistance Cole model thereby avoiding capacitive error impacts upon reactance measurements. Main results. The resistance–frequency approach allowed analysis of 100% of data files obtained from BIS measurements in 72 babies compared to 87% successful analyses with the Cole model. Resistance–frequency modelling error (percentage standard error of the estimate) was half that of the Cole method. Estimated resistances at zero and infinite frequency were used to predict body composition. Resistance-based prediction of fat-free mass (FFM) exhibited a 30% improvement in the two-standard deviation limits of agreement with reference FFM measured by air displacement plethysmography when compared to Cole model-based predictions. Significance. This study has demonstrated improvement in the analysis of BIS data based on the resistance frequency response rather than conventional Cole modelling. This approach is recommended for use where BIS data are compromised by high frequency capacitive leakage errors such as those obtained in babies and infants.

Measuring Cardiorespiratory Fitness without Exercise Testing: The Development and Validation of a New Tool for Spanish Adults.

Background: Cardiorespiratory fitness (CRF) is an important component of overall physical fitness and is associated with numerous health benefits, including a reduced risk of heart disease, diabetes, and obesity. However, direct measurement of CRF is time-consuming and therefore not feasible for screening purposes. Methods: A maximal treadmill exercise test with the Bruce protocol was performed to estimate VO2max in 1047 Spanish men and women aged 17 to 62 years. Weight, height, and heart rate were measured. Leisure-time physical activity (LTPA) was recorded using the Minnesota Leisure Time Physical Activity Questionnaire. A multiple linear regression model was developed to predict exercise-based VO2max. The validity of the model was examined by correlation, concordance, Bland-Altman analysis, cross-validation, and construct validity analysis. Results: There was no significant difference between VO2max obtained by the Bruce protocol (43.56 mL/kg/min) or predicted by the equation (43.59 mL/kg/min), with R2 of 0.57, and a standard error of the estimate of 7.59 mL/kg/min. Pearson's product-moment correlation and Lin's concordance correlation between measured and predicted CRF values were 0.75 and 0.72, respectively. Bland-Altman analysis revealed a significant proportional bias of non-exercise eCRF, overestimating unfit and underestimating highly fit individuals. However, 64.3% of participants were correctly classified into CRF tertile categories, with an important 69.9% in the unfit category. Conclusions: The eCRF equation was associated with several cardiovascular risk factors in the anticipated directions, indicating good construct validity. In conclusion, the non-exercise eCRF showed a reasonable validity to estimate true VO2max, and it may be a useful tool for screening CRF.

Development of a deep-learning algorithm for age estimation on CT images of the vertebral column

PurposeThe accurate age estimation of cadavers is essential for their identification. However, conventional methods fail to yield adequate age estimation especially in elderly cadavers. We developed a deep learning algorithm for age estimation on CT images of the vertebral column and checked its accuracy. MethodFor the development of our deep learning algorithm, we included 1,120 CT data of the vertebral column of 140 patients for each of 8 age decades. The deep learning model of regression analysis based on Visual Geometry Group-16 (VGG16) was improved in its estimation accuracy by bagging. To verify its accuracy, we applied our deep learning algorithm to estimate the age of 219 cadavers who had undergone postmortem CT (PMCT). The mean difference and the mean absolute error (MAE), the standard error of the estimate (SEE) between the known- and the estimated age, were calculated. Correlation analysis using the intraclass correlation coefficient (ICC) and Bland-Altman analysis were performed to assess differences between the known- and the estimated age. ResultsFor the 219 cadavers, the mean difference between the known- and the estimated age was 0.30 years; it was 4.36 years for the MAE, and 5.48 years for the SEE. The ICC (2,1) was 0.96 (95 % confidence interval: 0.95–0.97, p < 0.001). Bland-Altman analysis showed that there were no proportional or fixed errors (p = 0.08 and 0.41). ConclusionsOur deep learning algorithm for estimating the age of 219 cadavers on CT images of the vertebral column was more accurate than conventional methods and highly useful.

Impacts of urban emissions and air quality in São Paulo State, Brazil.

Daily violations of air quality have an impact on urban populations and cause damage to the environment. Thus, the study evaluated the violations of the daily concentrations of SO2, NO2, and PM10, in regions of the State of São Paulo (SSP), based on the National Environment Council (CONAMA) resolution no 491/2018 and the World Health Organization (WHO - World Health Organization. (2016). Ambient air pollution: a global assessment of exposure and burden of disease.) criteria. Daily SO2, NO2, and PM10data from 6 air quality stations operated by Environmental Company of the State of São Paulo CETESB (1996-2011) were organized and submitted to quality control, with data faults (gaps) being identified. The imputation of data via spline proved satisfactory in filling in the gaps (r > 0.7 and low values of Standard Error of the Estimate (SEE) and Root Mean Square Error (RMSE). The cluster analysis (CA) applied to SO2 formed only one homogeneous group (G1). Contrariwise, NO2 and PM10 formed two homogeneous groups (G1 and G2) each. The stations that showed the greatest similarity according to the CA were Cerqueira Cesar and Osasco. The cophenetic matrix generated for SO2 (0.83), NO2 (0.79), and PM10 (0.77) indicate a satisfactory adjustment of the dendrograms. The exploratory statistics applied to groups G1 and G2 point to the high variability of outliers. The WHO criteria are more restrictive than CONAMA regarding daily violations, with a reduction in SO2 and an increase in specific years for NO2 and PM10. Such variability is due to the adoption of public policies by the SSP and the influence of meteorological systems, being confirmed by the Run test that indicated oscillations in the time series, mainly in PM10, and also recognized well-defined biannual cycles.

Reference Standards of Cardiorespiratory Fitness Measured With the Cardiopulmonary Exercise Test Using the Treadmill in Chinese Adults Younger Than 60 Years.

Cardiorespiratory fitness (CRF) is a strong predictor of cardiorespiratory diseases and varies by race. The purpose of this study was to provide CRF reference standards and a prediction equation for peak oxygen uptake (V˙O 2peak ) from treadmill-based cardiopulmonary exercise testing (CPX) in Chinese individuals. Healthy participants (n = 4199) who completed a CPX using a treadmill were studied. The percentiles of V˙O 2peak were determined for four age groups (decades). A regression prediction model was developed from the derivation cohort (n = 3361), validated in the independent validation cohort (n = 838), and compared with the widely used Wasserman equation and the Fitness Registry and the Importance of Exercise National Database (FRIEND) equation. The mean V˙O 2peak values of four age groups (20-29, 30-39, 40-49, and 50-59 yr) were 42.6, 41.2, 38.7, and 35.9 mL/kg/min, respectively, for men, and 37.1, 34.7, 32.0, and 30.3 mL/kg/min, respectively, for women. The 50th percentiles of relative V˙O 2peak decreased with age for both sexes. The prediction equation was: Absolute V˙O 2peak (mL/min) = 236.68 - (504.64 × sex [male = 0; female = 1]) + (21.23× weight [kg]) - (14.31 × age [yr]) + (9.46 × height [cm]) (standard error of the estimate = 379.59 mL/min, R2 = 0.66, P < .001).Percentage predicted V˙O 2peak for the validation sample was 100.2%. The novel equation performed better than the other two equations. This study reports the first CRF reference standards and prediction equation generated from treadmill CPX in China. These reference standards provide a framework for interpreting the CRF of the Chinese population and could be useful information for a global CRF database.

Assessment of Clinical Reasoning in Undergraduate Medical Education: A Pragmatic Approach to Programmatic Assessment.

Clinical reasoning, a complex construct integral to the practice of medicine, has been challenging to define, teach, and assess. Programmatic assessment purports to overcome validity limitations of judgments made from individual assessments through proportionality and triangulation processes. This study explored a pragmatic approach to the programmatic assessment of clinical reasoning. The study analyzed data from 2 student cohorts from the University of Utah School of Medicine (UUSOM) (n = 113 in cohort 1 and 119 in cohort 2) and 1 cohort from the University of Colorado School of Medicine (CUSOM) using assessment data that spanned from 2017 to 2021 (n = 199). The study methods included the following: (1) asking faculty judges to categorize student clinical reasoning skills, (2) selecting institution-specific assessment data conceptually aligned with clinical reasoning, (3) calculating correlations between assessment data and faculty judgments, and (4) developing regression models between assessment data and faculty judgments. Faculty judgments of student clinical reasoning skills were converted to a continuous variable of clinical reasoning struggles, with mean (SD) ratings of 2.93 (0.27) for the 232 UUSOM students and 2.96 (0.17) for the 199 CUSOM students. A total of 67 and 32 discrete assessment variables were included from the UUSOM and CUSOM, respectively. Pearson r correlations were moderate to strong between many individual and composite assessment variables and faculty judgments. Regression models demonstrated an overall adjusted R2 (standard error of the estimate) of 0.50 (0.19) for UUSOM cohort 1, 0.28 (0.15) for UUSOM cohort 2, and 0.30 (0.14) for CUSOM. This study represents an early pragmatic exploration of regression analysis as a potential tool for operationalizing the proportionality and triangulation principles of programmatic assessment. The study found that programmatic assessment may be a useful framework for longitudinal assessment of complicated constructs, such as clinical reasoning.

Modelling commercial biomass of native yerba mate in the South-Center region of Paraná

The cultivation of the yerba mate species is of great importance, as in addition to helping to preserve forest remnants, it is also a source of income for rural producers. However, incorrect management can result in low productivity in yerba mate cultivation. Therefore, it is of great importance to evaluate the production of this species, using appropriate techniques. Thus, the present study aimed to evaluate the commercial biomass (BC) of a native yerba mate cultivation system, as well as adjust equations to estimate commercial biomass individually and per hectare for yerba mate in similar conditions. The study area is located on a private property in the municipality of Inácio Martins, where permanent sampling units were installed with a total area of 1.25 ha, in which data were collected from all yerba mate individuals that were pruned to obtain commercial biomass, and some dendometric variables were then measured. Based on the collected variables, commercial biomass estimates were made by applying the Stepwise Forward procedure. In addition to individual BC estimates for yerba mate, equations were also adjusted for estimates per unit area (ha). To evaluate the fit of the equations, the following criteria were used: the adjusted Coefficient of Determination (R²aj), the Standard Error of the Estimate in percentage (Syx%) and the graphical distribution of the residuals. The Stepwise procedure adjusted the best equations, with values for the coefficient of determination above 0.8 for individual estimates and above 0.9 for estimates per hectare and errors between 20 and 30% for both situations. Estimates of commercial yerba mate biomass using a single, easily obtainable variable showed errors of around 40% and heterogeneity in the graphic dispersion of residues. The use of the Stpewise tool can enable a better economic assessment of yerba mate managed areas.

Body composition estimates using a 2D image analysis system across different environmental conditions: An agreement study.

This study examined the agreement between %Fat measurements using a smartphone-based application (IMAGE) across different environmental conditions. A single reference image was obtained using an 8 MP smartphone camera under Ambient Light in front of a white background. Additional photos were obtained using a 0.7 MP, 5 MP, and 12 MP smartphone cameras; low-, moderate-, and bright-lighting conditions; and various color backgrounds including black, green, orange, and gray. %Fat measured using the 0.7 MP camera (27.8 ± 6.2 %Fat) was higher than the reference (26.8 ± 6.1 %Fat) (p < 0.001). The black (32.0 ± 12.0 %Fat), green (27.5 ± 6.3 %Fat), and gray (27.8 ± 6.3 %Fat) backgrounds yielded higher %Fat than the white (p = 0.03, 0.01, and 0.001). All camera, lighting, and background conditions were strongly correlated with the reference (all intraclass correlation coefficient [ICC] >0.98, all standard error of the estimate [SEE] <1.5 %Fat, all p < 0.001), except the black background which yielded poorer agreement with the white background (ICC = 0.69, SEE = 4.5%, p < 0.001). %Fat from IMAGE were strongly correlated across various environmental conditions.

COVID-19 pandemic and population density problem in Indonesia: transmigration policy as an alternative program

The objective of this study is to examine the associations between levels of population density and the transmission of COVID-19 in Indonesia. Descriptive analysis is employed to determine the data distribution of the response variable (dependent variable Y) as well as the independent variables X1 and X2. Therefore, inferential analysis is a quantitative technique that involves examining a sample in order to draw conclusions about a larger population. The present investigation has revealed a statistically significant positive connection (r = 0.954) between the incidence of COVID-19 patients and population density in Indonesia. The Model Summary provides the R value (Correlation Coefficient) of 0.959, indicating a strong positive correlation between the variables. Additionally, the multiple correlation coefficient r (Multiple R) is 0.920, which represents the determination index or the proportion of the influence of X on Y. Therefore, it can be asserted that 92% of COVID-19 cases are driven by population density and the number of individuals experiencing employment terminations, whilst the remaining 8% is affected by additional factors. The regression equation can be constructed from the Coefficients table in the following manner: The equation can be expressed as . The calculation of the Standard Error of the Estimate (SE) yields a value of 8151.076 or 8151. The standardized coefficient (Beta) of 1.381 represents the extent of association between the number of COVID-19 infection cases and population density.

A Reference Equation for Peak Oxygen Uptake for Pediatric Patients Who Undergo Treadmill Cardiopulmonary Exercise Testing

Pediatric patients are often referred to cardiopulmonary exercise testing (CPET) laboratories for assessment of exercise-related symptoms. For clinicians to understand results in the context of performance relative to peers, adequate fitness-based prediction equations must be available. However, reference equations for prediction of peak oxygen uptake (VO2peak) in pediatrics are largely developed from field-based testing, and equations derived from CPET are primarily developed using adult data. Our objective was to develop a pediatric reference equation for VO2peak. Clinical CPET data from a validation cohort of 1,383 pediatric patients aged 6 to 18years who achieved a peak respiratory exchange ratio ≥1.00 were analyzed to identify clinical and exercise testing factors that contributed to the prediction of VO2peak from tests performed using the Bruce protocol. The resultant prediction equation was applied to a cross-validation cohort of 1,367 pediatric patients. Exercise duration, gender, weight, and age contributed to the prediction of VO2peak, generating the following prediction equation: (R2=0.645, p <0.001, standard error of the estimate=6.19ml/kg/min): VO2peak (ml/kg/min) =16.411+ 3.423 (exercise duration [minutes]) - 5.145 (gender [0=male, 1=female]) - 0.121 (weight [kg])+0.179 (age [years]). This equation was stable across the age range included in the present study, with differences ≤0.5ml/kg/min between mean measured and predicted VO2peak in all age groups. In conclusion, this study represents what we believe is the largest pediatric CPET-derived VO2peak prediction effort to date, and this VO2peak prediction equation provides clinicians who perform and interpret exercise tests in pediatric patients with a resource with which to better quantify fitness when CPET is not available.

Outsourcing Strategies and Human Resource Performance in Tea Manufacturing Companies in Rwanda: A case of Ekaterratearwanda Limited

The objective of this research was to study the human resource outsourcing practices among tea manufacturing companies in Rwanda. With specific objective being, To determine the influence of outsourcing strategies and recruitment process on human resource performance at Ekaterra Tea Rwanda Limited, to assess how outsourcing strategies on employee benefits management influence human resource performance at Ekaterra Tea Rwanda Limited and To establish how outsourcing strategies influences employee relations and human resource performance at Ekaterra Tea Rwanda. All the tea manufacturing companies were targeted population of 100 respondent from the factories. The research design to be used was description survey and data was collected from one of the tea manufacturing company operating in Rwanda called Ekaterra Tea Rwanda Ltd. The respondents were comprised mostly of Management team and supervisors with experience in various departments. Questionnaires using semi structured questions were used to collect data. Primary data were collected from the respondents and were supplemented by secondary data from journals, magazines and supplements and analyzed using descriptive statistics such as frequencies, mean scores, standard deviations and percentages. The findings were presented in form of tables. The sample population of 80 determined using Slovin‘s formula were used. Simple random sampling were used to identify the people to answer questions. At Ekaterra tea manufacturing company, study used purposive sampling method to interview top managers of the project in order to get detailed information of the subject matter. Data were collected by using questionnaires, interviews and documentary sources. Using the statistical package for social sciences (SPSS), the collected data was presented using tables and figure while interpretation and analysis were done based on percentages, mean and frequencies of respondents views. The data was also analyzed using correlation techniques and regression technique. In summary, the information provides key statistics to assess the performance of a regression model. The R² value indicates that the model explains approximately 28% of the variance in the dependent variable, and the adjusted R² adjusts for the number of predictors. The standard error of the estimate provides information about the typical prediction error in the model. These statistics help in understanding the model's goodness of fit and its ability to explain the variation in the dependent variable. Keywords: Outsourcing, Human resource management, Outsourcing Strategies, Recruitment process, Employee’s benefits, Employee’s relations.

Predicting the Weight of Holstein-Friesian Males at Different Ages by some Body Measurements

This study was conducted in one of the private fields for breeding Holstein-Friesian calves in the village of Al-Hamad in Al-Diwaniyah Governorate, where 107 males were used, including 21 at the age of 1 year, 51 at the age of 2 years, and 35 at the age of 3 years. The animals were weighed with an electronic scale, and body measurements (height body, chest girth and hip girth in centimeters) and the study aims to find the best single or double size to estimate the body weight of Holstein Friesian males, and the results of the current study can be summarized as follows: It was found that age had a highly significant effect (p<0.01) on body weight and its measurements, as it increased with age, as the weights of Holstein Friesian males, aged 1, 2, and 3 years, for all ages reached 271.714, 359.627, 522.914, and 395.785 kg, respectively. Age also had a significant (p<0.01) effect on measurements of body length, chest and hip girths, and the correlation coefficients ranged between body measurements for males and for the sum of ages between 0.887 to 0.988. In this study, it was found that the best measure for estimating body weight is chest and hip girth, as the correlation coefficients between body weight and chest girth ranged between 0.845 to 0.965, and between weight and chest girth from 0.831 to 0.970. And that the best equation for estimating the body weight of bulls at the age of one year is the chest girth as a single measurement if the regression equation Y=-76.791+1.280GC. The coefficient of determination, R2 = 0.930, with a standard error of the estimate 5.59. As for the best equation for estimating the weight of bulls at the age of 2 years, it is based on the girth of the chest, as the determination reached R2 = 0.715, and the regression equation Y=-348.104+4.206CG. With a standard error of the estimate 25.625. And the girth of the hip, as the coefficient of determination was 0.753 = R2 and the regression equation y = - 431.986 + 4.647 CG. And with a standard error of the estimate 23.838. The best equation for estimating the weight of bulls at the age of 3 years is to rely on chest girth as a single measure and the regression equation. Y=-451.388+5.05 CG, And the determination equation is R2 = 0.877, with a standard error of the estimate 20.518. As for the regression equation to estimate the body weight of bulls and for all ages, the equation of dependence on the girth of the chest can be used Y=-610.582+5.813 CG. Where it was R2= 0.931 with a standard error of estimation was 27.936 or depending on the girth of the hip Y = -632.404 + 5.884 CG, where R2 = 0.941 HG and with a standard error of estimation was 25.725.

Relationship Between Body Mass Index and Body Fat Percentage in a Group of Indian Participants: A Cross-Sectional Study From a Tertiary Care Hospital.

Background Body mass index (BMI) is an important indicator of overweight and obesity. Unlike BMI, body fat percentage (BF%) can be utilized to estimate body composition regardless of weight and height. The association between BMI and BF%, as well as the impact of age and gender, may help estimate the prevalence of obesity more clearly. This study aimed to assess the relationship between BMI and BF%, examine the effect of age and gender on this relationship, and establish the linearity/curvilinearity of this relationship. Methodology The body composition analysis of 1,150 participants in various institutional events (institution foundation day) during 2019 and 2023 was performed using the Accuniq bio-electrical impedance analyzer (BIA) (Accuniq, Netherlands). The participants included undergraduate, postgraduate medical, and PhD students, as well as employees of All India Institute of Medical Sciences, New Delhi. Age groups were categorized as under 17 years, young adults (18-25 years), adults (26-44 years), middle-aged adults (45-59 years), and older adults (≥60 years). Pearson's correlation coefficient (r) was used for analyzing the relationship between BMI and BF%. To assess the effect of age and gender on this relationship, multiple regression analysis was applied, and polynomial regression was applied to test its linearity. The data were analyzed using SPSS version 25 (IBM Corp., Armonk, NY, USA). Results Males made up a larger proportion of the participants (56.3%; 647). The mean age of the participants was 36.5 ± 13.6 years. The mean BMI and BF% were 24.7 ± 4.0 kg/m2 and 29.1 ± 8.7%, respectively. A significant and moderate positive correlation (r = 0.630, p < 0.01) was observed between BMI and BF%. The mean ± SD of BMI and BF% had a directly proportional relationship with age. Among both genders, females showed a greater correlation (r = 0.852). Both age and gender had a significant effect on this relationship, with gender impacting more than age (β = 0.488, p < 0.000). The curvilinear nature of the relationship between BMI and BF% was demonstrated with the female model showing a more precise fit (R2 = 0.72, standard error of the estimate = 3.3%). Conclusions The relationship between BMI and BF% was significant and positive in this group ofIndians. This relationship was significantly impacted by age and gender and was curvilinear in nature. Females had a higher association than males between BMI and BF%. The study suggests that BMI, BF%, and the effects of age and gender should be taken into consideration when predicting obesity.

Assessment of venous pressure by compression sonography of the internal jugular vein during 3days of bed rest.

Compression sonography has been proposed as a method for non-invasive measurement of venous pressures during spaceflight, but initial reports of venous pressure measured by compression ultrasound conflict with prior reports of invasively measured central venous pressure (CVP). The aim of this study is to determine the agreement of compression sonography of the internal jugular vein (IJVP) with invasive measures of CVP over a range of pressures relevant to microgravity exposure. Ten healthy volunteers (18-55years, five female) completed two 3-day sessions of supine bed rest to simulate microgravity. IJVP and CVP were measured in the seated position, and in the supine position throughout 3days of bed rest. The range of CVP recorded was in line with previous reports of CVP during changes in posture on Earth and in microgravity. The correlation between IJVP and CVP was poor when measured during spontaneous breathing (r=0.29; R2 =0.09; P=0.0002; standard error of the estimate (SEE)=3.0mmHg) or end-expiration CVP (CVPEE ; r=0.19; R2 =0.04; P=0.121; SEE=3.0mmHg). There was a modest correlation between the change in CVP and the change in IJVP for both spontaneous ΔCVP (r=0.49; R2 =0.24; P<0.0001) and ΔCVPEE (r=0.58; R2 =0.34; P<0.0001). Bland-Altman analysis of IJVP revealed a large positive bias compared to spontaneous breathing CVP (3.6mmHg; SD=4.0; CV=85%; P<0.0001) and CVPEE (3.6mmHg; SD=4.2; CV=84%; P<0.0001). Assessment of absolute IJVP via compression sonography correlated poorly with direct measurements of CVP by invasive catheterization over a range of venous pressures that are physiologically relevant to spaceflight. However, compression sonography showed modest utility for tracking changes in venous pressure over time. NEW FINDINGS: What is the central question of this study? Compression sonography has been proposed as a novel method for non-invasive measurement of venous pressures during spaceflight. However, the accuracy has not yet been confirmed in the range of CVP experienced by astronauts during spaceflight. What is the main finding and its importance? Our data show that compression sonography of the internal jugular vein correlates poorly with direct measurement of central venous pressures in a range that is physiologically relevant to spaceflight. However, compression sonography showed modest utility for tracking changes in venous pressure over time.

Reliability and Validity of Different Lower-Limb Strength Tests to Determine 1RM in the Keiser A300 Leg Press.

Larsen, F, Loturco, I, Sigvaldsen, E, Strand, MF, Kalhovde, JM, and Haugen, T. Reliability and validity of different lower-limb strength tests to determine 1RM in the Keiser A300 leg press. J Strength Cond Res 37(10): 1963-1968, 2023-The aim of this study was to explore the reliability and validity of different lower-limb strength tests to determine the one-repetition maximum (1RM) value in the Keiser A300 leg press. Twenty-eight recreationally active subjects performed load-velocity (L-V) relationship, 1RM, isometric midthigh pull (IMTP), and maximal repetitions to failure (MRF) tests on 3 separated sessions. Predicted 1RMs for the L-V relationship were estimated from a linear regression equation, correlating movement velocity and relative loads. The number of repetitions from the MRF tests (at loads relative to bodyweight) and peak force from the IMTP tests were used in regression equations to predict 1RM. The level of significance was set to ρ ≤ 0.05. All 1RM prediction methods were highly comparable with the traditional 1RM test, as only trivial and nonsignificant differences were observed. Furthermore, the L-V relationship was the most reliable (intraclass correlation coefficient [± 95% confidence interval] = 0.99 [0.98, 0.996]; effect size = -0.01 [-0.38, 0.36], standard error of the measurement = 6.4 kg; coefficient of variation = 3.0 [2.2-3.8]% and valid (r = 0.95 [0.89, 0.98], effect size = 0.08 [-0.29, 0.45], standard error of the estimate = 20.4 kg; coefficient of variation = 7.4 [5.5-9.3]%) when compared with direct 1RM measurements. The L-V relationship test showed a significant change score relationship (r = 0.41 [0.04, 0.68]) against the direct 1RM measurements. In conclusion, the tests used in this study cannot be used interchangeably, but they represent a good alternative in training settings where 1RM testing is not feasible.

Determining critical power and W′ in running: Accuracy of different two-point models using the power metric

This study aims to determine the validity of the critical power (CP) and the work capacity over CP (W′) obtained from different two-time trial combinations with respect a five-point model. In a 3-week training period, 15 athletes (age: 23 ± 5 years; height: 166 ± 6 cm; body mass: 58 ± 8 kg; 5 km season-best: 15:29 ± 00:53 mm:ss) performed five time-trials (i.e. 3, 4, 5, 10, 20 min) on a 400 m track, from which the mean power outputs were obtained through the Stryd Power Meter. An acceptable level of agreement was considered if the following criteria were met: low bias and standard error of the estimate (SEE) (&lt;14 W [values corresponding to the ±5% of the mean CP]; W′: &lt;2.0 kJ [values corresponding to the ±10% of the mean W′]), R2 &gt; 0.90, and ICC &gt; 0.81. The CP presented an acceptable SEE for CPwork (1.3 ± 0.5%) and CP1/time (2.7 ± 1.1%) when using the five time-trials. For both CP models, the 3–10 min was the shortest valid combination, whereas the 3–20, 4–20, and 5–20 min showed the greatest level of agreement. The W′ presented a high SEE for CPwork (14.1 ± 5.2%) and CP1/time (13.8 ± 6.2%) when using the five time-trials, therefore, none of the two time-trials combinations were considered. The CP parameter can be accurately estimated from different two time-trial combinations, whereas none reached an acceptable level of accuracy for the determination of W′.

Comparison of Hb A1c Quantification in the Presence of Hemoglobin Variants of an HPLC Assay with an Enzymatic Assay.

In this study, we evaluated the impact of hemoglobin (Hb) variants on the performance of the Abbott Alinity c and Bio-Rad Variant II Turbo 2.0 HPLC Hb A1c assays. The analytical performance of the Abbott Alinity c Hb A1c (enzymatic) assay was compared to the Bio-Rad Variant II Turbo 2.0 HPLC method using leftover whole blood EDTA samples with and without the presence of a hemoglobin variant. Assay precision was determined from an analysis of controls. Bias was estimated from analysis of a set of 40 samples analyzed by a Tosoh G8 HPLC instrument at the University of Missouri Diabetes Diagnostic Laboratory, an NGSP Secondary Reference Laboratory. Between-day precision was excellent for both methods (<3%). Bias met NGSP criteria of ±5% to target value. Correlation between the Alinity and Bio-Rad methods was good for patient samples without a hemoglobinopathy (y = 1.028x - 0.38, standard error of the estimate (SEE) = 0.16, n = 36, mean bias = -0.22). A total of 700 hemoglobin variant samples were evaluated on the 2 methods. Of the hemoglobin variants, 640/700 gave results on both methods: hemoglobin (Hb) S trait (n = 452), C trait (n = 131), D trait (n = 23), E trait (n = 26), and a mixture of other hemoglobinopathies (n = 8) including beta thalassemia, high hemoglobin F, transfused Hb SC, transfused Hb SD, and transfused Hb SS, or unknown variant. There was good agreement for the 640 Hb variants between the methods with a range of mean differences of -0.10 to +0.06 depending on the variant, but more variability (SEE 0.25 to 0.39). Sixty samples did not have paired results. To our knowledge, this study was the largest investigation of the effect of hemoglobinopathies on the Abbott Alinity c Hb A1c assay. Analytical performance varied depending on the specific hemoglobin variant trait when compared to the Bio-Rad Variant II Turbo 2.0 HPLC method.

Continuing genetic improvement and biases in genetic gain estimates revealed in historical UK variety trials data

ContextThe current pace of yield increase for major crops is not fast enough to meet future demand. Crop breeding programmes are under increasing pressure to improve existing crops further. Quantifying the contribution of these programmes to observed yield increases is important for evaluating their success and identifying if crop improvement goals are likely to be met. ObjectiveIn this paper we explore methods to study the genetic gain of two cereal species, wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.). Specifically, the objective of this research was to identify sources of bias in genetic gain estimates of UK variety trials data. MethodsGenetic gain was estimated for fungicide-treated and untreated UK winter wheat, winter barley and spring barley for 1982–2018 using UK National List and Recommended List variety trials data. Subsets of the winter wheat variety trials dataset were used to replicate shorter breeding cycles to quantify the impact of the number and choice of long-term check varieties on estimating genetic gain. ResultsWhile genetic and non-genetic contributions to changes in UK cereal performance are in line with previous estimates, we were able to identify previously undetected changes and biases in estimates of variety performance. Specifically, we observed an increasing yield difference between fungicide treated and untreated variety trials as varieties age, driven by both a breakdown in disease resistance and a previously unobserved long-term increase in yield as varieties age in treated trials. This shows that yields of long-term check varieties cannot be assumed to be stable over time. We found that genetic gain estimates were highly sensitive to the long-term check varieties chosen, whilst the inclusion of multiple checks decreased the standard error of the estimate. ConclusionThe estimation of genetic gain is highly susceptible to bias. We provide recommendations on how to reduce the risk of bias for estimating genetic gain. ImplicationsAccounting for sources of bias in genetic gain calculations is important in any programme of selection to prevent inaccurate quantification of yield progress.

Validation of skinfold equations and alternative methods for the determination of fat-free mass in young athletes.

To cross-validate skinfold (SKF) equations, impedance devices, and air-displacement plethysmography (ADP) for the determination of fat-free mass (FFM). Male and female youth athletes were evaluated (n = 91[mean ± SD] age: 18.19 ± 2.37 year; height: 172.1 ± 9.8 cm; body mass: 68.9 ± 14.5 kg; BMI: 23.15 ± 3.2 kg m-2; body fat: 19.59 ± 6.9%) using underwater weighing (UWW), ADP, and SKF assessments. A 3-compartment (3C) model (i.e., UWW and total body water) served as the criterion, and alternate body density (Db) estimates from ADP and multiple SKF equations were obtained. Validity metrics were examined to establish each method's performance. Bioelectrical impedance analysis (BIA), bioimpedance spectroscopy (BIS), and the SKF equations of Devrim-Lanpir, Durnin and Womersley, Jackson and Pollock (7-site), Katch, Loftin, Lohman, Slaughter, and Thorland differed from criterion. For females, Pearson's correlations between the 3C model and alternate methods ranged from 0.51 to 0.92, the Lin's concordance correlation coefficient (CCC) ranged from 0.41 to 0.89, with standard error of the estimate (SEE) ranges of 1.9-4.6 kg. For SKF, the Evans 7-site and J&P 3 Site equations performed best with CCC and SEE values of 0.82, 2.01 kg and 0.78, 2.21 kg, respectively. For males, Pearson's correlations between the 3C model and alternate methods ranged from 0.50 to 0.95, CCC ranges of 0.46-0.94, and SEE ranges of 3.3-7.6 kg. For SKF, the Evans 3-site equation performed best with a mean difference of 1.8 (3.56) kg and a CCC of 0.93. The Evans 7-site and 3-site SKF equations performed best for female and male athletes, respectively. The field 3C model can provide an alternative measure of FFM when necessary.