4-component Model Research Articles

Bring your own device to school for mathematics learning: Namibian students’ behavioural intentions

The bring-your-own-device policy (BYOD) in schools has recently attracted considerable research interest. BYOD allows students to learn subjects like mathematics using personal mobile devices. Accordingly, BYOD can increase students’ desire to learn mathematics in school. In the study we report on here, we assessed 9th grade students’ (n = 500) behavioural intentions to learn mathematics using personal mobile devices in school. Data were collected from 12 urban and rural Namibian public schools through a paper survey. Drawing on the theory of reasoned action with an added facilitating condition component, we predicted students’ behavioural intentions to learn mathematics based on BYOD. The descriptive results show that most students own personal mobile devices and are eager to use them as part of their learning in school. Principal component and confirmatory factor analyses validated the 4-component model. The results indicate satisfactory construct validity. Structural equation modelling was used to examine the influence of the factors on the students’ behavioural intentions. The structural equation modelling results show that the theory of reasoned action and facilitating condition factors predicted students’ behavioural intentions toward BYOD for mathematics learning in school. The findings suggest that educational policymakers should amend existing policies to allow students to learn mathematics using personal mobile devices in school.

Планирование выхода на пенсию в предпенсионном возрасте: первичная адаптация и валидизация опросника «Шкала процесса планирования выхода на пенсию» Дж.Х. Нуна, К. Стивенс и Ф.М. Альпасс

<p>The paper presents the results of the initial adaptation and validation of the «The Process of Retirement Planning Scale» questionnaire, developed by a team of New Zealand scientists. Verification of the reliability and discriminativeness of the questionnaire subscales, gender differences and the model of retirement preparation were performed on a sample of 337 respondents (43 to 65 years old). Convergent validity was checked on a sample of 202 respondents (45 to 63 years old). Results showed that questionnaire has good psychometric measures and preserve 4-component model of retirement preparedness on a sample of Russian pre-retirees. Final version of questionnaire includes 35 items divided into 4 subscales and has a high degree of reliability-consistency: «Retirement representations» (Cronbach's alpha = 0,933), «Retirement goals» (Cronbach's alpha = 0.862), «The decision to prepare for retirement» (Cronbach's alpha = 0.855), «Preparing for retirement» (Cronbach's alpha = 0.854). «The Process of Retirement Planning Scale» is a reliable instrument measuring the stages of the process under study and can be used in psychological studies of pre-retirement planning.</p>

On Expectation-Maximization Algorithm with Split and Merge in R

This paper presents a comprehensive study on the implementation of the Expectation-Maximization (EM) algorithm for a 4-component bivariate Gaussian Mixture Model (GMM) with a focus on incorporating the split and merge techniques. The bivariate GMM is widely utilized in various fields, such as pattern recognition, image processing, and data clustering, due to its flexibility in capturing complex data distributions. Our proposed implementation leverages the versatility of the R programming language to create a robust and efficient framework for modeling and optimizing the parameters of the 4-component GMM. The EM algorithm is employed as a powerful tool for iteratively estimating the model parameters, ensuring convergence to a local maximum of the likelihood function. To enhance the model’s flexibility, we introduce the split and merge strategies, enabling the algorithm to adapt to diverse data structures and efficiently manage the complexity of the mixture components. The split operation allows for the subdivision of components when needed, while the merge operation facilitates the combination of components that represent similar patterns. This adaptive approach contributes to the model’s ability to capture intricate data patterns and improve convergence during the optimization process. The implementation is validated through extensive experimentation on synthetic, demonstrating its effectiveness in accurately estimating the parameters of the 4-component bivariate GMM. The proposed methodology proves to be a valuable addition to the existing tools available for GMM based modeling, providing researchers and practitioners with a flexible and powerful framework for analyzing complex data structures in diverse applications

EMOTIONAL INTELLIGENCE PROFILES AND INTERGENERATIONAL COLLABORATION IN BUSINESS

Emotional Intelligence (EI) fosters positive synergies for an intergenerational match in business. The main goal of this contribution is to investigate the components of ЕI for selected young people and seniors and to formulate recommendations for their successful intergenerational business cooperation in Slovakia. The 4-component instrumental model for EI in business and the “Emotional Intelligence in Business” questionnaire were used to construct an average EI profile for different generations. 157 young people (Generation Z) and 236 seniors (Baby Boomers generation) from Slovakia participated in the research. The statistical study of the components of EI was conducted using unadjusted and adjusted univariate tests for repeated measures and multiple comparisons. The results of both tests were identical and therefore considered robust. The research confirms that EI in business increases with the respondent’s age: investigated seniors demonstrate a higher level of EI than selected young people in three components. At the same time, findings show that seniors are more focused on self-awareness, while the young are more empathic. The research considerably deepens the understanding of the potential contributions of different generations in business collaboration and benefits social unity and sustainable economic development.

MIEX resin and enhanced coagulation treatment of high-bromide natural water: Chlorine reactivity and DBP precursors removal

Both organic and inorganic constituents play a major role in the formation and speciation of disinfection by-products (DBPs) under chlorination. In this study, the removal of organic and inorganic precursors to DBPs by ion exchange and coagulation was examined, and the relationships between characteristics of dissolved organic matter (DOM), chlorine reactivity, and the formation of DBPs in high-bromide natural water were investigated. Formation potential tests were conducted for trihalomethanes (THMs) and haloacetronitriles (HANs) before and after the different treatments (coagulation, magnetic ion exchange (MIEX) resin, and combined MIEX/coagulation). At a low resin dose, MIEX was more effective than coagulation for DOM removal. The advantages of the MIEX resin were more prominent at a high resin dose because substantial amounts of DOM and bromide were simultaneously removed. The maximum bromide uptake on the MIEX resin was 0.02 mg-Br/mL. A 4-component PARAFAC model was developed using a dataset of 148 fluorescence excitation-emission matrices (EEMs) generated from pre-chlorinated and post-chlorinated samples. The maximum fluorescence intensities of humic-like components were found to be strongly correlated to THMs formation (R2 = 0.79–0.89). The MIEX treatment substantially lowered the formation of THMs (19–51%) and HANs (70–95%) due to its ability to remove humic-like DOM and bromide. The fluorescence loss for humic-like DOM was found to increase over a chlorination time of 3 days, suggesting that these compounds had stronger reactivity to chlorine compared to protein-like DOM. The chlorine reactivity of protein-like DOM increased after treatments as the residual chlorine was able to react with the less reactive fractions of DOM after chlorine-reactive compounds were removed.

A four-component mixing model reveals snowpack melting as early as March during the 2019 hydrological year in the Upper Ganga Basin

Ice and snow meltwater are important components of the glacier-fed Himalayan rivers that control the seasonal streamflow pattern. With the ongoing global warming, there remains significant uncertainty regarding how melting Himalayan glaciers will impact the water runoff. Here, we quantified the rainwater, ice meltwater, snow meltwater, and groundwater flow in the river using a δ18O, d, and Cl- -based 4-component hydro-geochemical mixing model. At a weekly resolution, we measured the stable water isotopes (18O/16O and 2H/1H, expressed as δ18O and δD), Cl- and Na+ near the river Alaknanda mouth, in the Upper Ganga Basin over one year (2018–2019). The weekly scale time-series geochemical record captured the subtle variability of ice and snow meltwater flow in the Alaknanda River. The two most significant observation was (i) an onset of snow meltwater was observed as early as in March during the study period contrary to our present understanding that the onset of snow meltwater usually happens in April, and (ii) a significant increase in ice and snow meltwater flow during the peak monsoon season. The early onset of snow meltwater runoff in the Himalayan rivers could be an important concern as this could potentially reduce the flow of snow meltwater during the early summer months when water demand is highest due to intense agricultural activities in the mountainous region. Similarly, an increase in ice meltwater runoff in the monsoon season increases the risks of major flood events. Since weekly scale time-series data of ice meltwater, snow meltwater, rainwater, and groundwater flow in glacier-fed Himalayan rivers over the entire hydrological year was largely missing, this study provides an important dataset to better understand the year-long dependency of the Himalayan rivers on the glaciers critical to improving the accuracy and predictive power of hydrologic models.

Body composition and body mass index measures from 8 to 18 years old in children with cystic fibrosis

BackgroundHistorically, body mass index (BMI) >50th percentile has represented optimal nutritional status in children with cystic fibrosis (CF) due to its positive association with lung function. Body composition parameters including fat-free mass index (FFMI) have been suggested as a more physiological nutrition benchmark. Aims: (1) describe changes in body composition with age and gender; (2) assess the correlation between measures of nutritional status (FFMI-z, FMI-z, BMI-z) and lung function (forced expiratory volume in one second predicted; FEV1pp). MethodsThis retrospective, mixed cross-sectional and serial measures study consisted of children with CF (8 to 18 years) attending Sydney Children's Hospital (2007–2020). FFMI and fat mass index (FMI) were taken from biennial dual energy x-ray absorptiometry (DXA) scans. Z-scores were derived using Well's reference population [1]. Repeated measures correlation analyses assessed correlations between FFMI-z, FMI-z, and BMI-z with FEV1pp. Results339 DXA reports were analysed from 137 patients. There were slight downwards trends in BMI-z and FMI-z, and an upwards trend in FFMI-z with increasing age and across both genders. Females had higher FMI-z and FFMI-z than males from 12.5 years. There was a weak, positive correlation between FEV1pp and BMI-z (r = 0.14, p = 0.04), and FFMI-z (r = 0.25, p<0.001). FMI-z had no correlation with FEV1pp (r=-0.06, p = 0.41). ConclusionDeficits in FFMI exist despite increasing trends with age. FFMI-z and BMI-z had a weak, positive correlation with FEV1pp. In contemporary cohorts, nutritional status (reflected by surrogate markers such as FFMI and BMI) may be less influential upon lung function than in previous decades.[1]: Wells, J.C., et al. Body-composition reference data for simple and reference techniques and a 4-component model: a new UK reference child. Am. J. Clin. Nutr.96, 1316–1326 (2012).

Prognostic Value of Microscopic Tumor Necrosis in Adrenal Cortical Carcinoma

Adrenal cortical carcinoma (ACC) is an uncommon neoplasm with variable prognosis. Several histologic criteria have been identified as predictors of malignancy in adrenal cortical tumors. The Weiss score is the system most widely employed for diagnostic purposes, but also possesses prognostic value. We aim to determine the relative impact of each Weiss parameter on ACC patient survival. A multicenter retrospective analysis was conducted on a series of 79 conventional ACCs surgically treated at the Florence and Utrecht centers of the European Network for the Study of Adrenal Tumors (ENSAT). Weiss classification was recapitulated using principal component analysis (PCA). The Kaplan–Meier and Cox multivariate regression analyses were applied in order to estimate the prognostic power of Weiss versus other clinical parameters. PCA reduced the nine Weiss parameters to the best fitting 4-component model, each parameter clustering with a single component. Necrosis and venous invasion clustered together with the highest scores, thus establishing the most relevant component (Component 1) to explain Weiss distribution variability. Only Component 1 significantly predicted overall survival (OS, log-rank = 0.008) and disease-free survival (DFS, log-rank < 0.001). When considering the prognostic power of Weiss parameters, necrosis alone could independently assess OS (log-rank = 0.004) and DFS (log-rank < 0.001) at both the Kaplan–Meier and multivariate Cox regression analyses [hazard ratio (HR) = 7.8, 95% confidence interval [CI] = 1.0–63.5, p = 0.05, and HR = 12.2, 95% CI = 1.6–95.0, p = 0.017, respectively]. The presence of necrosis significantly shortened time to survival (TtS) and time to recurrence (TtR), 57.5 [31.5–103.5] vs 34 [12–78] months (p = 0.05) and 57.5 [31.5–103.5] vs 7 [1.0–31.5] months (p < 0.001), respectively. Our study suggests that, of the Weiss parameters, necrosis is the most powerful adverse factor and the best predictor of OS and DFS in ACC patients.

Fluorescent signatures of autochthonous dissolved organic matter production in Siberian shelf seas

The East Siberian Sea is an area of high biogeochemical activity caused by multiple factors, such as an influence of river runoff, Atlantic, and Pacific waters, formation and melting of sea ice, and internal circulation. Extensive amounts of carbon are accumulated in sub-sea permafrost within the Arctic shelf in East Siberia. Thawing permafrost and resulting microbial decomposition of frozen carbon pool is a potential feedback process affected global climate dynamics. Several studies of dissolved organic carbon (DOC) distribution in the East Siberian Sea have demonstrated untypical for other Arctic shelf seas pronounced non-conservative DOC behavior. Using seawater samples from the 69th cruise of R/V Akademik Mstislav Keldysh conducted in the Laptev and East Siberian seas in September 2017, this study examines the distribution of fluorescent dissolved organic matter (FDOM) along the shelf-crossing transects including the areas affected by the Khatanga, Lena, Indigirka, and Kolyma river runoff. The set of 137 excitation-emission matrices (EEMs), analyzed with Parallel Factor analysis (PARAFAC), was described by a 4-component model demonstrating Tucker’s congruence coefficient above the 0.95 threshold. Spectral characteristics of the resulting components allowed identifying them as well-known humic-like A and C, protein-like B/T, and marine humic-like M fluorophores. Component C1 (A fluorophore) showed a good correlation with salinity for different river plume influenced regions. As well as chromophoric DOM (CDOM) absorption, it can be used as marker of input of terrestrial DOM to the Arctic Ocean. A distinctive feature of the East Siberian Sea shelf waters in comparison with the Laptev Sea is the higher content of the C2 and C4 components, exhibiting protein-like and marine humic-like fluorescence. Component C3 (C fluorophore) which is usually identified as terrestrial-derived material, was found to be produced locally on the East Siberian Sea shelf and in the continental slope region of the Khatanga transect. Destruction of dead algae cells, production of marine biota and dissolved organic matter (DOM) reworking are considered as possible autochthonous FDOM sources in the Siberian shelf seas.

Body Composition and Determination of Somatotype of the Spanish Elite Female Futsal Players

Background: In any sport, it is of the utmost importance to identify variables capable of positively influencing scores. Futsal is a sport of intermittent high-intensity intervals that requires the athletes’ physical, technical, and tactical skills. There are no specific recommendations on anthropometry–body composition for this sport. The aim of this study was to describe the somatotype and the anthropometric characteristics, body composition, and somatotype of a group of Spanish elite female futsal players of the first-division league. Methods: Eighty-eight players (24 ± 4.94 years) from 14 teams of the first women’s division of the Spanish Futsal League were evaluated. Measurements were taken according to the anthropometric protocol of the International Society for the Advancement of Kinanthropometry (ISAK). Body composition (BC) and somatotype were calculated according to the 4-component model and Heath-Carter, respectively. Results: Pivot and goalkeeper positions were most different from the rest. Both were the heaviest positions and presented the largest mesomorphic data (4.13 ± 1.29 and 3.67 ± 0.95), kg of bone mass (10.60 ± 1.00 and 10.37 ± 1.08), and kg of lean mass (29.80 ± 1.25 and 29.12 ± 2.12), for the pivot and goalkeeper, respectively. Conclusion: The evaluation and evolution of the somatotype and BC is an assessment tool that could be a useful guide for technical and medical staff.

Hierarchical Symptom Components in Early Psychosis.

Quantitative models of psychopathology can empirically guide subclassification of heterogeneous clinical presentations such as psychosis; they are particularly well-equipped to capture the nuanced symptomatology observed in first-episode psychosis. As well, components may be better aligned with biological variables. The current study sought to confirm and extend knowledge of the hierarchical structure of psychosis symptoms in first-episode psychosis. Based on past hierarchical work, we hypothesized that a 4 component level would be most closely associated with longitudinal disability. Participants with early-stage psychosis (N = 370) underwent clinical assessment with the scale for the assessment of positive symptoms (SAPS), scale for assessment of negative symptoms (SANS), and global assessment scale(GAS). A subset was assessed at 6 months (N = 221) and 1 year (N = 207). Hierarchical symptom components were extracted at 12 levels. The predictive utility of the components for global functioning was tested. As predicted, the 4-component model (reality distortion, thought disorder, inexpressivity, apathy/asociality) provided a superior prediction of functioning over other levels of the hierarchy. Baseline apathy/asociality longitudinally predicted functioning beyond the shared variance of the components at 6 months (b = -4.83, t(216) = -5.37, p < .001, R2adj = 0.12) and 1-year (b = -4.49, t(202) = -4.38, p < .001, R2adj = 0.09). The hierarchical structure of psychotic symptomatology and its external validity have been robustly established in independent, longitudinal first-episode psychosis samples. The established model incorporates multiple levels of granularity that can be flexibly applied based on the level that offers the greatest predictive utility for external validators.

Creating a better learning environment: a qualitative study uncovering the experiences of Master Adaptive Learners in residency

BackgroundAdaptive expertise is an important physician skill, and the Master Adaptive Learner (MAL) conceptual model describes learner skills and behaviors integral to the acquisition of adaptive expertise. The learning environment is postulated to significantly impact how MALs learn, but it is unclear how these successful learners experience and interact with it. This study sought to understand the authentic experience of MALs within the learning environment and translate those experiences into practical recommendations to improve the learning environment for all trainees.MethodsFollowing a constructivist paradigm, we conducted a thematic analysis of transcripts from focus groups composed of MALs to identify commonalities in experiences and practices of successful postgraduate trainees in the learning environment. Saturation was achieved after seven focus groups, consisting of thirty-eight participants representing fourteen specialties from four institutions. Researchers coded transcripts using constant comparison analysis, which served as the foundation for our thematic analysis.ResultsWe identified eight themes and situated them within a 4-component model of the learning environment. Four themes were identified within the personal component: (1) patients drive learning; (2) learning has no endpoint; (3) management of emotions is crucial for learning; (4) successful learning requires a structured approach. Two themes were identified in the social component: (5) positive social relationships are leveraged to maximize learning; (6) teaching facilitates personal learning. Two themes were identified in the organizational component: (7) transitions challenge learners to adapt; (8) the learning environment dictates goal setting strategy. No major themes were identified in the physical/virtual component, although participants frequently used technology when learning.ConclusionsMaster Adaptive Learners experience similar facilitators of, and barriers to, success in the learning environment. Overall, our data show that acquisition of many successful strategies and skills that support learning are relegated to the hidden curriculum of residency training. Educators could support a more effective learning environment for all trainees by: (1) highlighting patients as the focal point of learning, (2) building a professional ‘learner’ identity, (3) teaching learning skills, and (4) creating opportunities for collaborative learning.

Temporal Differences in Concussion Symptom Factors in Adolescents following Sports-Related Concussion

To evaluate temporal differences in concussion symptoms up to 30days following a sports-related concussion. Adolescent and young adult athletes (n=782) were separated based on time since injury at presentation as Early (0-7days; n=321, age: 15.4±1.9years, 51.7% female), Middle (8-14days; n=281, age: 15.8±2.2years, 54.8% female), and Late (15-30days; n=180, age: 15.6±1.8years, 52.8% female). All participants completed the 22-item Post-Concussion Symptom Scale at first visit. A confirmatory factor analysis was completed separately for each time since injury cohort using a 4-component model reported previously. The confirmatory factor analysis model fit was acceptable for Early, Middle, and Late (using cognitive-migraine-fatigue, affective, sleep, and somaticfactors). Both affective (change=0.30; P=.01; Cohen d=0.30) and sleep (change=0.51; P≤.001; Cohen d=0.47) factors were significantly greater in the Late group compared with the Early, but not Middle, groups. The previously reported 4-factor symptom model, including cognitive-migraine-fatigue, affective, somatic, and sleep factors, was appropriate for adolescents up to 30days' postinjury. However, adolescents who presented between 15 and 30days' postinjury reported greater affective and sleep symptoms than those who presented within 1week. Clinicians should consider these temporal differences when evaluating concussion symptoms in adolescents, as greater affective and sleep symptoms can be predictive of prolonged recovery/persistent complications.

Bayesian Exploration of 4-Component Rayleigh Mixture Model with Wind Speed Data Application

This article focuses on the application of wind speed data of 4 coastal areas of Baluchistan, that is, Gawadar, Jiwani, Ormara, and Pasni on 4-Component Rayleigh Mixture Model (4-CRMM) under Bayesian context. Type I right censoring scheme is used because it is popular in reliability theory and survival analysis. To accomplish the objective, the Bayes estimates (BEs) of the parameter of the mixture model along with their posterior risks (PRs) using informative prior (IP) and noninformative prior (NIP) are obtained. Hyperparameters are obtained by employing the prior predictive method. BEs are calculated under two distinct loss functions, squared error loss function (SELF) and modified squared error loss function (MSELF). The statistical properties and performance of the BEs under said loss functions are also evaluated by simulation study for different sample sizes.

Soft Target Weight: Theory and Simulation of a Novel Haemodialysis Protocol Which Reduces Excessive Ultrafiltration

Introduction: Excessive ultrafiltration is associated with intra-dialytic symptoms, loss of residual function, and mortality in haemodialysis patients. A major contributor to excessive ultrafiltration is within-individual variation in pre-dialysis weight and the concept of achieving a fixed target weight by the end of each dialysis session. Haemodialysis protocols which allow variable post-dialysis weight have not been proposed. Methods: Weight variation was observed in haemodialysis patients and healthy controls to estimate the proportion of pre-dialysis weight variation which could be considered natural variation. These estimates were used to derive a novel protocol for setting ultrafiltration, which was evaluated by mathematical modelling. Results: Amongst 20 haemodialysis patients, mean (SD) pre-dialysis weight was 102.74 (0.94)% of target weight after a 2-day gap and 103.50 (0.94)% after a 3-day gap. Amongst 10 healthy individuals, mean (SD) daily weight was 100.0 (0.71)% of average weight. A 4-component model of pre-dialysis weight was derived using these estimates, in which the best estimate of pre-dialysis excess fluid is the midpoint of excess weight and average fluid gain, and used to propose a novel protocol for ultrafiltration setting. In simulations, the novel protocol reduced ultrafiltration variation by more than half (standard deviation 0.6 vs. 1.3% of target weight, p < 0.001), without increasing the variation in post-dialysis fluid excess. Excessive ultrafiltration rates (over 13 mL/h/kg) were far less frequent using this protocol (2.6% vs. 7.5% of sessions, p = 0.001). Conclusion: Considering natural weight variation allows the development of a novel protocol for ultrafiltration in which target weight does not have to be achieved precisely: it is therefore a soft target. This protocol, which is predicted to substantially reduce excessive ultrafiltration variation, is a zero-cost intervention with the potential to improve symptoms and clinical outcome for haemodialysis patients.

PhaseKinetics for assessing the compositional evolution of petroleum generated from the early to Late Miocene source rock, Belait Formation, Brunei-Muara district, Brunei Darussalam

The Early to Late Miocene Belait Formation in Brunei-Muara district has been investigated using the PhaseKinetic approach for compositional petroleum generation kinetics. The source rock characteristics of the selected coal and coaly shale samples indicate kerogen type III-II and type III with moderate HI recorded in the range of 156–258 mg HC/g TOC. The Rock Eval pyrolysis data also show immature organic matter with maximum recorded Tmax values of 415 °C. Pyrolysis gas chromatography (PyGC) reflects that the source rocks generated petroleum grading from paraffinic-naphthenic-aromatic (P–N-A) high wax to gas and condensate, which can be interpreted as heterogeneous kerogen derived from terrigenous higher land plant material. The main activation energies range of two selected coal samples (S24 and S27) are located at 49–52 kcal/mol accounting each for only 15% of the total bulk reaction. Assuming a 3 °C/My geologic heating rate, calculated onset temperatures for the generated hydrocarbon at transformation ration, TR = 10% were 105–120 °C for the two samples. The end of hydrocarbon generation at approximately TR = 95% is expected to occur at temperatures around 170 °C and above. MSSV pyrolysis gas chromatography affirms the existence of high wax paraffinic kerogen type–III–II with some marine input for one end member sample (S24). The 4-component compositional kinetic model indicates that the maximum generation of different petroleum components of sample S24 is predicted at lower values of temperature and vitrinite reflectance if compared with sample S27. The other end member of sample (S27) is more typically humic coal-like with type-III kerogen generating mostly gas and condensate.

Fundamental underpinnings of simulation education: describing a four-component instructional design approach to healthcare simulation fellowships

Although in 2020, there are more than 120 healthcare simulation fellowships established globally, there is a paucity of literature on how to design fellowship programs most effectively, to equip graduates with the knowledge, skills, and attitudes of a competent simulation educator. Offering a systematic structure to approach simulation fellowship programmatic design may aid in better achieving program goals. In this manuscript, we present the application of the 4-component instructional design model as a blueprint to the development of Simulation Education Fellowships. We offer examples used at the NYC Health + Hospitals simulation fellowship to illustrate how the 4-component model informs fellowship program design which promotes the development of a simulation educator. This manuscript will provide a roadmap to designing curricula and assessment practices including self-reflective logbooks to focus the path toward achieving desired skills and shape future conversations around programmatic development.

Psychometric validation of the modified Naranjo algorithm used in homeopathy for chronic cases.

The modified Naranjo algorithm assesses the physician assigned cause-effect relationship for homeopathic medicines. It is being adopted in homeopathy researches, but not yet validated systematically. We intended to validate the modified Naranjo algorithm by examining its psychometric properties. An online survey sought agreement of 25 experts on the 10 items of the tool on 5-point agreement scale. Next, 285 responses from collected prospectively from chronic cases enrolled under the clinical verification program of the council in 2018 were subjected to testing of construct validity using exploratory principal component analysis (PCA). Confirmatory factor analysis (CFA; n=150) was performed to verify the goodness-of-fit of the model. Reliability was tested using internal consistency, test-retest reliability, and inter-rater reliability by kappa statistics. Experts' responses mean values were 4 or higher (i.e. responses were relevant) and standard deviations were less than 1 (i.e. less heterogeneous). In PCA using varimax, all the items loaded above the pre-specified value of 0.4 and identified 4 components explaining 64.1% of variation. The goodness-of -fit of the 4-component CFA model was acceptable (chi-square 89.253, p<0.001). Internal consistency (Cronbach's alpha 0.7) was borderline; test-retest reliability was acceptable. Kappa statistics was moderate to fair, but poor for few of the items. Statistical evaluations indicate that the modified Naranjo algorithm is useful, but needs improvement.

Predicting Adaptations to Resistance Training Plus Overfeeding Using Bayesian Regression: A Preliminary Investigation.

Relatively few investigations have reported purposeful overfeeding in resistance-trained adults. This preliminary study examined potential predictors of resistance training (RT) adaptations during a period of purposeful overfeeding and RT. Resistance-trained males (n = 28; n = 21 completers) were assigned to 6 weeks of supervised RT and daily consumption of a high-calorie protein/carbohydrate supplement with a target body mass (BM) gain of ≥0.45 kg·wk−1. At baseline and post-intervention, body composition was evaluated via 4-component (4C) model and ultrasonography. Additional assessments of resting metabolism and muscular performance were performed. Accelerometry and automated dietary interviews estimated physical activity levels and nutrient intake before and during the intervention. Bayesian regression methods were employed to examine potential predictors of changes in body composition, muscular performance, and metabolism. A simplified regression model with only rate of BM gain as a predictor was also developed. Increases in 4C whole-body fat-free mass (FFM; (mean ± SD) 4.8 ± 2.6%), muscle thickness (4.5 ± 5.9% for elbow flexors; 7.4 ± 8.4% for knee extensors), and muscular performance were observed in nearly all individuals. However, changes in outcome variables could generally not be predicted with precision. Bayes R2 values for the models ranged from 0.18 to 0.40, and other metrics also indicated relatively poor predictive performance. On average, a BM gain of ~0.55%/week corresponded with a body composition score ((∆FFM/∆BM)*100) of 100, indicative of all BM gained as FFM. However, meaningful variability around this estimate was observed. This study offers insight regarding the complex interactions between the RT stimulus, overfeeding, and putative predictors of RT adaptations.

The Importance of Hydration in Body Composition Assessment in Children Aged 6-16 Years

Body composition is associated with many noncommunicable diseases. The accuracy of many simple techniques used for the assessment of body composition is influenced by the fact that they do not take into account tissue hydration and this can be particularly problematic in paediatric populations. The aims of this study were: (1) to assess the agreement of two dual energy X-ray absorptiometry (DXA) systems for determining total and regional (arms, legs, trunk) fat, lean, and bone mass and (2) to compare lean soft tissue (LST) hydration correction methods in children. One hundred and twenty four healthy children aged between 6 and 16 years old underwent DXA scans using 2 GE healthcare Lunar systems (iDXA and Prodigy). Tissue hydration was either calculated by dividing total body water (TBW), by 4-component model derived fat free mass (HFFMTBW) or by using the age and sex specific coefficients of Lohman, 1986 (HFFMLohman) and used to correct LST. Regression analysis was performed to develop cross-calibration equations between DXA systems and a paired samples t-test was conducted to assess the difference between LST hydration correction methods. iDXA resulted in significantly lower estimates of total and regional fat and lean mass, compared to Prodigy. HFFMTBW showed a much larger age/sex related variability than HFFMLohman. A 2.0 % difference in LST was observed in the boys (34.5 kg vs 33.8 kg respectively, p < 0.05) and a 2.5% difference in the girls (28.2 kg vs 27.5 kg respectively, p < 0.05) when corrected using either HFFMTBW or HFFMLohman. Care needs to be exercised when combining data from iDXA and Prodigy, as total and regional estimates of body composition can differ significantly. Furthermore, tissue hydration should be taken into account when assessing body composition as it can vary considerably within a healthy paediatric population even within specific age and/or sex groups.