Standard Error Of The Estimate Research Articles

Cross-Validation of Equations for Estimating 1 Repetition Maximum From Repetitions to Failure for the Bench Press and Leg Extension.

Roberts, TD, Smith, RW, Arnett, JE, Ortega, DG, Schmidt, RJ, and Housh, TJ. Cross-validation of equations for estimating 1 repetition maximum from repetitions to failure for the bench press and leg extension. J Strength Cond Res XX(X): 000-000, 2024-Eighteen previously published equations (EQs) that estimate 1 repetition maximum (1RM) from repetitions to failure (RTF) were cross-validated for the bench press (BP) and leg extension (LE) movements. Forty-three recreationally active men (age: 20.58 ± 1.47 years; body mass [BM]: 81.66 ± 13.65 kg) completed a 1RM test and RTF at 80% of the 1RM test for the LE, and 39 of the 43 men (age: 20.61 ± 1.48 years; BM: 83.58 ± 12.73 kg) completed the same tests for the BP. The EQs were categorized as generic (the source did not indicate its applicability for a specific movement) or movement-specific (BP-specific and LE-specific EQs). The generic EQs were cross-validated for both movements, whereas the BP-specific and LE-specific EQs were cross-validated for their respective movements only. The cross-validation criteria included calculations of the constant error (CE) (mean differences between estimated and measured 1RM), Pearson Correlation Coefficient, standard error of the estimate, and total error. The level of significance was set at p ≤ 0.05. After the initial cross-validation analyses of the previously published EQs, the most accurate EQs were modified by subtracting their cross-validation CE from the original EQ to improve their accuracy for estimating BP and LE 1RM by eliminating systematic error. The modified EQs were then cross-validated using the same statistical procedures. Based on the cross-validation analyses, we recommend the following EQs: BP 1RM = (RTF0.1 × weight) + 1.49 and LE 1RM = (RTF0.1 × weight) + 1.06 using weights that result in 4-10 RTF.

Maximum heart rate prediction equations based on field-tests with adequate clinical applicability for aerobic exercise prescription to individuals after stroke

Objectives 1) to develop two maximum heart rate (HRmax) prediction equations for individuals after stroke; 2) to investigate its validity. Methods The HRmax was obtained by a Cardiopulmonary Exercise Test (CPET). Participants also completed the Six-minute Walking Test (6MWT) and the Incremental Shuttle Walking Test (ISWT). Results 60 individuals (54(12) years; 64(69) months after stroke) were included. Twenty individuals (58(10) years; 67(61) months after stroke) were included in the cross-validation group. For the first model, the following equation was generated (equation-1): HRmax= 87.655 + 0.726 (HRpeak in the 6MWT) − 0.386 (age in years), (R2=0.53; Standard Error of the Estimate (SEE)= 15.35; p < 0.0001). For the second model, the following equation was generated (equation-2): HRmax= 96.523 + 0.681 (HRpeak in the ISWT) − 0.039 (walking distance in meters in the ISWT) − 0.400 (age in years), (R2=0.53; SEE = 15.51; p < 0.0001). Significant and high magnitude agreement was found between the HRmax obtained by the CPET and the predicted HRmax by equation-1 (ICC= 0.85; 95% CI= 0.63 − 0.94) and equation-2 (ICC= 0.72; 95% CI= 0.29 − 0.89). Conclusions Two HRmax prediction equations have been developed and showed adequate validity. Professionals will have the option of choosing one of the two equations to use.

Development and validation of a new anthropometric equation to predict fat mass percentage in a white Spanish population.

(1) To develop a new regression equation for estimating fat mass percentage (%FM) from anthropometric measurements in a heterogeneous Caucasian population and (2) to compare it with the Durnin and Womersley equation, which is one of the most used anthropometric equations for fat mass assessment. Body mass, stature, and four skinfolds (biceps, triceps, subscapular, and supracrestal) were assessed by an accredited anthropometrist, according to the International Society for Advancement in Kinanthropometry. Participants completed a dual-energy X-ray absorptiometry (DXA) whole-body scan to determine their %FM. A new anthropometric equation to estimate %FM was developed using multiple forward regression analyses with DXA as the reference method. Tests for the accuracy of the different equations included mean differences, coefficient of determination, standard error of the estimate (SEE), concordance correlation coefficient (CCC), and Bland-Altman plots. Spain. Two hundred and eighteen healthy Caucasian participants aged 18-65 years participated in this cross-sectional study. Our proposed equation explained 89.9% of the variance in the DXA-derived %FM, with a low random error (SEE = 3.00%), a very strong agreement (CCC = 0.93), no fixed or proportional bias, and a relatively low individual variability (5.84%). However, the Durnin and Womersley equations obtained a fixed bias of -3.65% when compared to DXA and a greater individual variability (6.74%). The proposed equation can accurately estimate %FM in a heterogeneous Caucasian population with a wide age range (18-65 years). Additionally, the Durnin and Womersley equation was inadequate when applied to our participants.

Power or speed: Which metric is more accurate for modelling endurance running performance on track?

This study aimed to compare the accuracy of the power output, measured by a power meter, with respect to the speed, measured by an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) sport watch to determine the critical power (CP) and speed (CS), work over CP (W') and CS (D'), and long-duration performance (i.e., 60 min). Fifteen highly trained athletes randomly performed seven time trials on a 400m track. The CP/CS and W'/D' were defined through the inverse of time model using the 3, 4, 5, 10, and 20 min trials. The 60 min performance was estimated through the power law model using the 1, 3, and 10 min trials and compared with the actual performance. A lower standard error of the estimate was obtained when using the power meter (CP: 2.7 [2.1-3.3] % and W': 13.8 [10.4-17.3] %) compared to the speed reported by the IMU (CS: 3.4 [2.5-4.3] %) and D': 20.7 [16.6-24.7] %) and GNSS sport watch (CS: 3.4 [2.5-4.3] % and D': 20.6 [16.7-24.7] %). A lower coefficient of variation was also observed for the power meter (4.9 [3.7-6.1] %) Regarding the speed reported by the IMU (10.9 [7.1-14.8] %) and GNSS sport watch (10.9 [7.0-14.7] %) in the 60 min performance estimation, the power meter offered lower errors than the IMU and GNSS sport watch for modelling endurance performance on the track.

ESTIMACIÓN DE LA REGIÓN DE CRECIMIENTO ESTÁNDAR DE GUAJOLOTES NATIVOS (Meleagris gallopavo L.) CALCULADA MEDIANTE LA ECUACIÓN DE RICHARDS

&lt;p&gt;&lt;strong&gt;Background. &lt;/strong&gt;A variety of mathematical models explain animal growth, showing a sigmoid curve. Richards' model includes four parameters, allowing a better fit to experimental data, as evidenced by a high coefficient of determination and a low standard error of estimation. However, it does not always converge and some of its parameters are difficult to interpret biologically. It is useful to model a standard growth zone to estimate, at a given age, the weight of native turkeys within confidence limits, to obtain useful management conclusions.&lt;strong&gt; Objective. &lt;/strong&gt;To establish a standard average growth region for male native turkeys on the coast of Oaxaca, for productive purposes, using the Richards equation. &lt;strong&gt;Methodology. &lt;/strong&gt;The weights of 23 male native turkeys were recorded weekly from hatching to 54 weeks of age. Non-linear regression to fit the data to Richards' equation was performed using the free software GeoGebra®. The standard growth zone consisted of a lower and upper limit, which were determined by adding and subtracting the standard error of the estimate from Richards' model. The growth rate equation and graph were developed, showing its asymmetry, maximum value and age at which said value is reached.&lt;strong&gt; Results. &lt;/strong&gt;Determination coefficient of 0.993, standard error of the estimate of 190.5 g, and maximum growth rate of 322.11 g*week-1 at 18.22 weeks of age were obtained.&lt;strong&gt; Implications. &lt;/strong&gt;The use of the standard growth zone, under intensive production conditions, is valuable in the study and evaluation of the growth of male native turkeys&lt;strong&gt;. Conclusions. &lt;/strong&gt;The standard growth zone is a very useful concept, allowing for the establishment of weight limits that animals should have at the optimal age, facilitating decision-making to optimize management and feeding practices, as well as maximizing efficiency and performance.&lt;/p&gt;

Validity and reliability of the T-Force and Chronojump systems to measure movement velocity on resistance machines with older adults

The current study aimed to analyze the validity and reliability of the T-Force and Chronojump systems to measure the movement velocity in the leg press (LP) and chest press (CP) exercises in older people. Eighteen older adults (6 men and 12 women, 79.9 ± 8.5 years) performed a set of procedures over three weeks: (i) the first week was to familiarize participants with the testing procedures, (ii) the second was to perform a progressive loading test until reaching one-repetition maximum (1RM) in the LP and CP, and (iii) in the third week, participants performed three repetitions against five loads (40, 50, 60, 70, and 80% of 1RM). The mean velocity of each repetition was recorded simultaneously through the T-Force and Chronojump devices. Linear regressions (coefficient of determination [r2] and standard error of the estimate [SEE]) analyzed the inter-device validity, and Bland-Altman plots illustrated the systematic differences between devices. A mixed-effects model estimated the mean velocity differences between devices. The relative reliability was analyzed by the intra-class correlation coefficient (ICC[1,k]), while the absolute reliability was by the standard error of measurement (SEM) and the coefficient of variation (CV). The results showed that the T-Force and Chronojump presented a high association level in measuring mean velocity in the LP and CP (r2 range: 0.96–0.99; SEE range: 0.01–0.02 m·s− 1) and low systematic bias (0.02–0.03 m·s− 1). The mean velocity values of T-Force were significantly higher than Chronojump only for 40% 1RM (p = 0.04). Excellent reliability inter-device (ICC range: 0.95–0.98; CV range: 1.7–3.2%) and intra-device (ICC range: 0.90–0.97; CV range: 3.4–6.5%) was observed. This study shows that the T-Force and Chronojump systems are valid and reliable for measuring movement velocity in the CP and LP machines when used by older adults.

Impact of Generalized Versus Individualized Load-Velocity Equations on Velocity-Loss Magnitude in Bench-Press Exercise: Mixed-Model and Equivalence Analysis.

This study analyzed the influence of 2 velocity-based training-load prescription strategies (general vs individual load-velocity equations) on the relationship between the magnitude of velocity loss (VL) and the percentage of repetitions completed in the bench-press exercise. Thirty-five subjects completed 6 sessions consisting of performing the maximum number of repetitions to failure against their 40%, 60%, and 80% of 1-repetition maximum (1RM) in the Smith machine bench-press exercise using generalized and individualized equations to adjust the training load. A close relationship and acceptable error were observed between percentage of repetitions completed and the percentage of VL reached for the 3 loading magnitudes and the 2 load-prescription strategies studied (R2 from .83 to .94; standard error of the estimate from 7% to 10%). A simple main effect was observed for load and VL thresholds but not for load-prescription strategies. No significant interaction effects were revealed. The 40% and 60% 1RM showed equivalence on data sets and the most regular variation, whereas the 80% 1-repetition maximum load showed no equivalence and more irregular variation. These results suggest that VL is a useful variable to predict percentage of repetitions completed in the bench-press exercise, regardless of the strategy selected to adjust the relative load. However, caution should be taken when using heavy loads.

A novel approach to predict core temperature during heat stress among firefighters

This study aimed to create a novel, non-invasive approach to predict core temperature (Tc) during heat stress among firefighters. BackgroundThe direct measure of Tc is typically performed through invasive techniques (rectal, esophageal, or intestinal). Existing predictive methods involve complex systems with multiple pieces of impractical equipment or are otherwise unsuitable for the work environment. Here, we hypothesized that a novel, non-invasive algorithm using variables collected from a single piece of commercially available equipment could effectively predict Tc. MethodsThe participants performed a steady-state exercise protocol in an environmental chamber (35 °C, 45% humidity) while donning firefighter personal protective equipment. The variables collected were skin temperature (Tsk), heart rate (HR), time, respiratory rate (RR), and rate of skin temperature acquisition per minute (Tsk/min). ResultsOf the variables collected, all contributed to the multiple regression model, except HR. Tsk/min was calculated using Tsk and time. The initial model created in this study predicted Tc with a standard error of the estimate (SEE) of 0.23 °C and an adjusted R2 of 0.897. Following a "leave-one-out" bootstrap method, a robust equation was created using mean coefficients. This robust equation predicted Tc with a SEE of 0.23 and an R2 of 0.902. DiscussionThis paper provides a practical, non-invasive model to predict Tc with minimal resources. This method has the potential to provide continuous monitoring of firefighters in the field and can be used as a metric to withdraw firefighters when under detrimental physiological stress. Ultimately, this could improve the health and longevity of firefighters.

Prediction of the maximum oxygen consumption (VO2max) for young Saudi females using exercise parameters

BACKGROUND: Maximum oxygen consumption (VO2max), an essential indicator of integrated function of the cardiovascular, respiratory, and muscular systems, is an excellent predictor of all-cause morbidity and mortality. Direct measurement is a sophisticated procedure which for some individuals is difficult to perform. The use of international formulae leads to overestimation or underestimation of values. Therefore, the present study aimed to generate a best-fit VO2max prediction formula for young Saudi females. MATERIALS AND METHODS: One hundred and two randomly selected young healthy Saudi females (19–25 years) with normal body mass index (18.5–24.99 kg m−2) and sedentary lifestyle underwent maximum cardiopulmonary exercise testing on cycle ergometer when using breath-by-breath analyzing system. Hemodynamic and ventilatory parameters were assessed before and during the maximum exercise. Significantly correlated exercise and nonexercise parameters enterd in multiple linear regression analysis to find the best-fit model, and used to generate a VO2max prediction formula. RESULTS: The mean measured VO2max was 1449.1 ± 233.92 ml min−1. The VO2max prediction formula was 1304.193 ± 17.902 × body weight + 43.857 × time until exhaustion − 6.83 × maximum heart rate. The mean calculated VO2max was 1449.14 ± 143.97 ml min−1. No statistically significant difference was observed between the measured and calculated VO2max. The Bland–Altman test for the limit of agreement was performed and showed good agreement between the two values with a minute proportional bias. The standard error of the estimate was 156.34, which is approximately 10.6% of the mean measured VO2max. Reliability analysis yielded a Cronbach’s alpha of 0.709. CONCLUSION: This study presents a reliable and valid VO2max prediction formula for young Saudi females. This nationally generated formula is far more representative of the VO2max value than international formulae. We recommend the implementation of this formula and an investigation of its validity in other Saudi population groups.

Estimation of the Average Yield of Cotton using Outlier Robust Geographically Weighted Regression Approach

The General Crop Estimation Survey (GCES) scheme requires a large number of Crop Cutting Experiments (CCEs) to be conducted to get a reliable estimate below the district level. However, conducting a large number of CCEs imposes a financial burden on Govt. agencies. Additionally, large-scale surveys like GCES often result in many outlier observations in the CCE data. To address this issue,this study was conducted to estimate the yield rate of cotton with a relatively fewer number of CCEs than the GCES scheme using the proposed Outlier Robust Geographically Weighted Regression (ORGWR) approach. Validation of the proposed methodology was done using the real CCE dataset of Amravati district for the 2012-13 agriculture year in Maharashtra. In this approach,the number of CCEs conducted for GCES scheme was reduced, and then this reduced number of the CCEs can be predicted using the proposed ORGWR approach. The predicted CCEs and the incomplete CCEs data are then combined to form a complete dataset. This complete dataset is used to calculate the crop yield accurately. The study conducted a comparison between the ORGWR approach and GCES methodology for estimating the average yield of cotton. The results showed that the ORGWR approach, when used with a lesser number of CCEs,yielded estimates that were almost equivalent to those obtained using the GCES methodology with the complete dataset. Moreover, the standard error of the estimate was reliable, indicating the validity of the results.

Prediction of Geometrical Characteristics of an Inclined Negatively Buoyant Jet Using Group Method of Data Handling (GMDH) Neural Network

A new approach to predicting the geometrical characteristics of the mixing behavior of an inclined dense jet for angles ranging from 15° to 85° is proposed in this study. This approach is called the group method of data handling (GMDH) and is based on the artificial neural network (ANN) technique. The proposed model was trained and tested using existing experimental data reported in the literature. The model was then evaluated using statistical indices, as well as being compared with analytical models from previous studies. The results of the coefficient of determination (R2) indicate the high accuracy of the proposed model, with values of 0.9719 and 0.9513 for training and testing for the dimensionless distance from the nozzle to the return point xr/D and 0.9454 and 0.9565 for training and testing for the dimensionless terminal rise height yt/D. Moreover, four previous analytical models were used to evaluate the GMDH model. The results showed the superiority of the proposed model in predicting the geometrical characteristics of the inclined dense jet for all tested angles. Finally, the standard error of the estimate (SEE) was applied to demonstrate which model performed the best in terms of approaching the actual data. The results illustrate that all fitting lines of the GMDH model performed very well for all geometrical parameter predictions and it was the best model, with an approximately 10% error, which was the lowest error value among the models. Therefore, this study confirms that the GMDH model can be used to predict the geometrical properties of the inclined negatively buoyant jet with high performance and accuracy.

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.

Nitric Oxide Donor Sodium Nitroprusside Reduces Racemic Ketamine-But Not Esketamine-Induced Pain Relief.

The anesthetic, analgesic and antidepressant drug ketamine produces dissociation with symptoms of psychosis and anxiety, an effect attributed to neuronal nitric oxide depletion following N-methyl-d-aspartate blockade. There is evidence that dissociation induced by racemic ketamine, containing both ketamine enantiomers (S- and R-ketamine) but not esketamine (the S-isomer) is inhibited by nitric oxide (NO) donor sodium nitroprusside (SNP). We tested whether a similar intervention would reduce racemic and esketamine-induced analgesia in a randomized double-blind placebo-controlled trial. Seventeen healthy volunteers were treated with 0.5 μg.kg-1.min-1 SNP or placebo during a 3-h infusion of escalating doses of racemic ketamine (total dose 140 mg) or esketamine (70 mg). Pain pressure threshold (PPT) and arterial blood samples for measurement of S- and R-ketamine and their metabolites, S- and R-norketamine, were obtained. The data were analyzed with a population pharmacokinetic-pharmacodynamic model that incorporated the measured S- and R- ketamine and S- and R-norketamine isomers as input and PPT as output to the model. The potency of the 2 formulations in increasing PPT from baseline by 100% was 0.47 ± 0.12 (median ± standard error of the estimate) nmol/mL for esketamine and 0.62 ± 0.19 nmol/mL for racemic ketamine, reflecting the 52 ± 27% lower analgesic potency of R-ketamine versus S-ketamine. Modeling showed that SNP had no effect on S-ketamine potency but abolished the R-ketamine analgesic effect. Similar observations were made for S- and R-norketamine. Since SNP had no effect on S-ketamine analgesia, we conclude that SNP interacts on R-ketamine nociceptive pathways, possibly similar to its effects on R-ketamine activated dissociation pathways.

AGE ESTIMATION USING DIAPHYSEAL LONG BONE LENGTHS IN A NON-ADULT SKELETAL SAMPLE FROM GONUR DEPE, TURKMENISTAN

Introduction. The aim of this study is to provide group-specific regression equations for age estimation of immature human skeletal remains younger than 12 years of age from the diaphyseal length of the six long bones. Further, to compare inverse and classical calibration models for age estimation and the suitability of previously published regression methods for archaeological and present-day populations in relation to the analyzed sample. Materials and methods. The studied sample consists of 128 non-adult individuals from Gonur Depe – the major BMAC site in Turkmenistan (2300–1500 BCE). Regression formulae were obtained for each of the six long bones, separately for entire sample (0–12 years of age) and for two subsamples below and above 2 years of age. For each equation the coefficient of determination (R2) and F-statistics were calculated. Residuals were tested for normality of distribution, autocorrelation, homoscedasticity, and the equality of mean to zero (one-sample t-test). Comparison between inverse and classical calibration was provided using paired samples t-test. To assess the applicability of other regression formulae to the studied sample both the mean residuals (MR) and mean of the absolute value of the residuals (MAR) were calculated, as an estimate of bias and accuracy respectively. Results. All regression models showed a strong statistical significance and high R2 value. The slope coefficients of the regression lines of diaphyseal length upon age are greater for the upper limb bones both in the entire sample and two subsamples separately. The lower limb bones are characterized by lower growth rates. In contrast to the inverse calibration, for the classical model the mean standard errors (MSE) were smaller for the upper limb bones rather than for the lower limb bones. For the lower limb bones the standard error of the estimate (SE) was generally smaller in inverse formulae. Comparison of both models for the femur however shows their equal performance. For the exception of proposed and classical calibration formulae all inverse models for femur diaphyseal length show consistent differences from zero in relation to the studied sample. Discussion. The inverse and classical calibration models as a technique for age estimation using diaphyseal long bone lengths are both equally applicable in the studied sample. It is recommended to use the equations for the lower limb bones in the inverse model and for the upper limb bones in the classical model. The Gonur Depe population is characterized by relatively higher growth rates of the upper limb bones and distal limb segments relative to the proximal ones. Most of the previously published inverse calibration models are not recommended for uncritically use due to the high risk of obtaining biased estimates on samples that are different chronologically and/or territorially.

Validity of equations for estimating fat-free mass in people living with the human immunodeficiency virus

Background & aimsEquations estimating fat-free mass (FFM) in people living with the human immunodeficiency virus (HIV) show differences in the validation process. The current study aimed to verify the validity of FFM estimation equations derived from bioelectrical impedance (BIA) in people living with HIV aged 40 years and older. MethodsA cross-sectional study was conducted with 68 participants evaluated using dual-energy X-ray absorptiometry (DXA) and by two BIA devices (Analyzer and Biodynamics). The study aimed to determine the validity of six different FFM equations from four different studies by Lukaski and Bolonchuk (1987), Kotler et al. (1996), Beraldo et al. (2015) and Hegelund et al. (2017). Comparisons were made using the t-test or Wilcoxon test. To verify the validity between DXA and two BIA devices, the following statistical analyses were performed: Lin's concordance correlation coefficient, intraclass correlation coefficient, coefficient of determination, standard error of the estimate, differences in the limits of agreement by Bland and Altman analysis, correlation between the average and the differences of the methods by Pearson or Spearman correlation. ResultsOnly equation 2 of Kotler et al. (1996) for males by Biodynamics BIA showed no difference in FFM. The Lin's concordance correlation coefficient was excellent (0.96), irrespective of sex, for Equation 2 of Kotler et al. (1996) by BIA Analyzer. All equations were reproducible (>0.85). The coefficient of determination ranged from 68% to 92%, and the standard error of the estimates ranged from 1.8 kg to 5.0 kg. The differences between the limits of agreement ranged from 7.2 kg to 14.9 kg, and the correlations between the average and the differences of the methods showed differences in FFM for three equations (p<0.01). ConclusionThe choice of equations must consider the equipment used and the sex of the sample investigated. Only Equation 2 of Kotler et al. (1996) was considered valid, irrespective of sex, to estimate the FFM by BIA Analyzer.

Forecasting and validating fat mass ratio models through anthropometric measurements and health-related factors among people with HIV: a cross-sectional investigation.

There is a limited research on predictive models of fat mass ratio (FMR) in people living with human immunodeficiency virus (HIV) (PWH). This study aimed to develop models considering anthropometric and health-related factors to predict and validate FMR in PWH regardless of sex. One hundred and six Brazilian PWH (46.4±9.8 years) were evaluated for body composition using dual-energy X-ray absorptiometry (DXA), body circumference (BC), and skinfold thicknesses (SKs). FMR predictive models were developed using stepwise linear regression, and their agreement with DXA was assessed using Bland-Altman plots. Cross-validation was performed using the predicted residual error sum of squares (PRESS) method. Six FMR estimation models were developed for PWH, with adjusted R2 ranging from 0.43 to 0.72, standard error of the estimate (SEE) from 0.16% to 0.22%, and 95% confidence interval (CI) from 1.03 to 1.15. Model 6, including thigh SK, waist BC, therapy duration, subscapular SK, education years, and abdominal SK, exhibited the highest determination power (R2 adjusted 0.72, SEE 0.16%, and 95% CI: 1.06-1.15). The agreement between DXA-based FMR and predictive models showed minimal bias (-0.03 to +0.04) and narrower limits of agreement, particularly for the top-performing model (-0.33 to +0.30). Model 6 exhibited a high adjusted Q2PRESS (0.70) and low SPRESS (0.17). Our predictive models advance the study of body composition in PWH by consolidating the use of anthropometry for diagnosing and monitoring lipodystrophy regardless of sex.

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

The 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±6.9years, weight: 70.9±13.1kg, and height: 163.5±7.4cm) 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)=0.05m.s-1 by LA/PA) and MPV (R2=0.900; SEE=0.06m.s-1 by LA and PA) were observed. In contrast, PV showed a weaker association with %1RM (R2=0.704; SEE=0.15m.s-1 by LA and PA). The MV and MPV of 1RM was 0.22±0.04m·s-1, whereas the PV at 1RM was 0.63±0.18m.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.