Continuous Independent Variables Research Articles

Acute-Phase Neurofilament Light and Glial Fibrillary Acidic Proteins in Cerebrospinal Fluid Predict Long-Term Outcome After Severe Traumatic Brain Injury.

This study investigated trajectory profiles and the association of concentrations of the biomarkers neurofilament light (NfL) and glial fibrillary acidic protein (GFAP) in ventricular cerebrospinal fluid (CSF) with clinical outcome at 1 year and 10-15 years after a severe traumatic brain injury (sTBI). This study included patients with sTBI at the Neurointensive Care Unit at Sahlgrenska University Hospital, Gothenburg, Sweden. The injury was regarded as severe if patients had a Glasgow Coma Scale ≤ 8 corresponding to Reaction Level Scale ≥ 4. CSF was collected from a ventricular catheter during a 2-week period. Concentrations of NfL and GFAP in CSF were analyzed with enzyme-linked immunosorbent assay. The Glasgow Outcome Scale (GOS) was used to assess the 1-year and 10-15-year outcomes. After adjustment for age and previous neurological diseases, logistic regression was performed for the outcomes GOS 1 (dead) or GOS 2-5 (alive) and GOS 1-3 (poor) or GOS 4-5 (good) versus the independent continuous variables (NfL and GFAP). Fifty-three patients with sTBI were investigated; forty-seven adults are presented in the article, and six children (aged 7-18 years) are described in Supplement 1. The CSF concentrations of NfL gradually increased over 2 weeks post trauma, whereas GFAP concentrations peaked on days 3-4. Increasing NfL and GFAP CSF concentrations increased the odds of GOS 1-3 outcome 1 year after trauma (odds ratio [OR] 1.73, 95% confidence interval [CI] 1.07-2.80, p = 0.025; and OR 1.61, 95% CI 1.09-2.37, p = 0.016, respectively). Similarly, increasing CSF concentrations of NfL and GFAP increased the odds for GOS 1-3 outcome 10-15 years after trauma (OR 2.04, 95% CI 1.05-3.96, p = 0.035; and OR 1.60, 95% CI 1.02-2.00, p = 0.040). This study shows that initial high concentrations of NfL and GFAP in CSF are both associated with higher odds for GOS 1-3 outcome 1 year and 10-15 years after an sTBI, implicating its potential usage as a prognostic marker in the future.

Childhood Obesity Trends among 8-11-Year-Olds: Insights from a School Sample in Vienna, Austria (2017-2023).

In Austria, childhood obesity is a public health concern. This study examined time trends in the percentage of obesity among a sample of schoolchildren from Vienna (2017-2023). The body mass index percentiles of 326 children [9.3 years old (95% CI 8.3-10.5, min-max 8.0-10.9] from the EDDY study were calculated for trend analyses. Trend analysis was performed using a logistic regression using overweight and obesity as dependent binary variables, and study year and age as independent continuous variables. The percentage change over time was calculated, including the first period of the COVID-19 pandemic. Obesity percentages increased from 23.5 (95% CI 15.1-31.9)% in 2017 to 25.0 (95% CI 12.2-37.8)% in 2023. From 2017 to 2023, while overweight/obesity percentages decreased by 25.9 (-59.5-15.6)%, obesity increased by 6.4 (-51.2-94.9)%. A non-significant trend (p ≥ 0.38) was observed. During the first period of the pandemic, the percentage changes in overweight/obesity and obesity were 68.4 (5.6-187.9)% and 29.2 (-37.3-166.8)%, respectively. The current percentage of obesity in this sample is high and peaked in 2020 during the pandemic. These findings emphasize the need for future investigations considering the representativeness of the school-aged population in Austria to gain a broader picture of overweight and obesity trends.

Comparison of Discriminant Analysis and Support Vector Machine on Mixed Categorical and Continuous Independent Variables for COVID-19 Patients Data

Purpose: Numerous factors can affect the duration of COVID-19 recovery. One method involves utilizing natural herbal medication. This study seeks to determine the variables influencing the duration of COVID-19 recovery and to compare discriminant analysis and support vector machine models using COVID-19 patient data from West Sumatra.Methods: Two data mining methods, Discriminant Analysis and Support Vector Machine with different types of kernels (linear, polynomial, and radial basis function), were employed to categorize the time of COVID-19 recovery in this work. The study utilized 428 data points, with 75% allocated for training data and 25% for testing data. The independent factors were evaluated by determining the selection variables' information value (IV) to gauge their influence on the dependent variable. Data resampling techniques were employed to tackle the problem of data imbalance. This study employs data resampling techniques, including undersampling, oversampling, and SMOTE. The balancing accuracy of Discriminant Analysis and Support Vector Machine was examined.Result: The Discriminant Analysis with SMOTE achieved a balanced accuracy of 66.50%, outperforming the linear kernel Support Vector Machine with SMOTE, which had a balanced accuracy of 63.20% in this dataset.Novelty: This study assessed the novelty, originality, and value by comparing Discriminant Analysis and SVM algorithms with categorical and continuous independent variables. This research explores techniques for managing imbalanced data using undersampling, oversampling, and SMOTE, with variable selection based on information value assessment.

Evaluation of Spin Bias in Systematic Reviews and Meta-analyses of Rotator Cuff Repair With Platelet-Rich Plasma.

The use of platelet-rich plasma (PRP) in orthopaedics continues to increase. One common use of PRP is as an adjunct in rotator cuff repair surgery. Multiple systematic reviews and meta-analyses have summarized the data on PRP use in rotator cuff repair surgery. However, systematic reviews and meta-analyses are subject to spin bias, where authors' interpretations of results influence readers' interpretations. To evaluate spin in the abstracts of systematic reviews and meta-analyses of PRP with rotator cuff repair surgery. Systematic review; Level of evidence, 3. A PubMed and Embase search was conducted using the terms rotator cuff repair and PRP and systematic review or meta-analysis. After review of 74 initial studies, 25 studies met the inclusion criteria. Study characteristics were documented, and each study was evaluated for the 15 most common forms of spin and using the AMSTAR 2 (A Measurement Tool to Assess Systematic Reviews, Version 2) rating system. Correlations between spin types and study characteristics were evaluated using binary logistic regression for continuous independent variables and a chi-square test or Fisher exact test for categorical variables. At least 1 form of spin was found in 56% (14/25) of the included studies. In regard to the 3 different categories of spin, a form of misleading interpretation was found in 56% (14/25) of the studies. A form of misleading reporting was found in 48% (12/25) of the studies. A form of inappropriate extrapolation was found in 16% (4/25) of the studies. A significant association was found between misleading interpretation and publication year (odds ratio [OR], 1.41 per year increase in publication; 95% CI, 1.04-1.92; P = .029) and misleading reporting and publication year (OR, 1.41 per year increase in publication; 95% CI, 1.02-1.95; P = .037). An association was found between inappropriate extrapolation and journal impact factor (OR, 0.21 per unit increase in impact factor; 95% CI, 0.044-0.99; P = .048). A significant amount of spin was found in the abstracts of systematic reviews and meta-analyses of PRP use in rotator cuff repair surgery. Given the increasing use of PRP by clinicians and interest among patients, spin found in these studies may have a significant effect on clinical practice.

Moving Frames: Difference and Differential-Difference Lagrangians

This paper develops moving frame theory for partial difference equations and for differential-difference equations with one continuous independent variable. In each case, the theory is applied to the invariant calculus of variations and the equivariant formulation of the conservation laws arising from Noether's theorem. The differential-difference theory is not merely an amalgam of the differential and difference theories, but has additional features that reflect the need for the group action to preserve the prolongation structure. Projectable moving frames are introduced; these cause the invariant derivative operator to commute with shifts in the discrete variables. Examples include a Toda-type equation and a method of lines semi-discretization of the nonlinear Schrödinger equation.

Ingresos en fines de semana y festivos por insuficiencia cardiaca descompensada y mortalidad intrahospitalaria. ¿Un efecto acumulativo de los «días no laborables»?

Introduction and objectivesThe aim of this study was to analyze whether nonelective admissions in patients with heart failure (HF) on nonworking days (NWD) are associated with higher in-hospital mortality. MethodsWe conducted a retrospective (2018-2019) observational study of episodes of nonelective admissions in patients aged 18 years and older discharged with a principal diagnosis of HF in acute general hospitals of the Spanish National Health System. NWD at admission were defined as Fridays after 14:00hours, Saturdays, Sundays, and national and regional holidays. In-hospital mortality was analyzed with logistic regression models. The length of NWD was considered as an independent continuous variable. Propensity score matching was used as a sensitivity analysis. ResultsWe selected 235 281 episodes of nonelective HF admissions. When the NWD periods were included in the in-hospital mortality model, the increases in in-hospital mortality compared with weekday admission were as follows: 1 NWD day (OR, 1.11; 95%CI, 1.07-1.16); 2 days (OR, 1.13; 95%CI, 1.09-1.17); 3 (OR, 1.16; 95%CI, 1.05-1.27); and ≥4 days (OR, 1.20; 95%CI, 1.09-1.32). There was a statistically significant association between a linear increase in NWD and higher risk-adjusted in-hospital mortality (chi-square trend P=.0002). After propensity score matching, patients with HF admitted on NWD had higher in-hospital mortality than those admitted on weekdays (OR, 1.11; average treatment effect, 12.2% vs 11.1%; P<.001). ConclusionsWe found an association between admissions for decompensated HF on an NWD and higher in-hospital mortality. The excess mortality is likely not explained by differences in severity. In this study, the “weekend effect” tended to increase as the NWD period became longer.Full English text available from:www.revespcardiol.org/en

Copula-based markov chain logistic regression modeling on binomial time series data

A first-order autoregressive time series model with binomial distributed random variables has been developed using the copula-based Markov chain model approach. By still utilizing conditional probability, covariate variables can also be included in the model and can be assumed as the independent variable. The time series dependent variable with a binomial distribution and continuous independent variables can be modelled using a copula-based Markov chain model with the probability of success expressed in the logit model. This study proposes a copula-based Markov chain logistic regression model with marginal binomial and joint distribution functions built through the copula function. Besides that, this study aims to estimate the parameters involved in the model. The parameters are the parameters of the logistic regression model as the relationship between the dependent and independent variables and the copula parameter as a time dependency. Using the bivariate copula functions are Clayton, Gumbel and Frank, the parameter estimation method is Maximum Likelihood Estimation (MLE). Simulations were carried out to see the efficiency of the parameter estimation and asymptotic results. Based on the simulation results, it was concluded that MLE provides an accurate estimate of the copula-based Markov chain logistic regression. In addition, the copula-based Markov chain logistic regression model can not only see the relationship between the independent and dependent variables but also provide an estimate of the time dependency of the dependent variable. The following are some of the proposed approach's highlights:•This method proposes a binomial time series data model with covariate variables by combining the logistic regression model and the first-order Markov chain model.•Parameter estimation in this model uses the Maximum Likelihood Estimation method.•The model provides the possibility to see the relationship between variables and the time dependency.

Effect of high-gravity acceleration forces and anti-gravity maneuver for cardiac function in fighter pilots

Abstract Background Environmental factors such as hypoxia and gravity affect to the human cardiovascular system. It’s more important for fighter pilots who are exposed to high altitude, harsh gravitational (G) acceleration, and perform anti-G maneuvers similar to the Valsalva maneuver. Experimental studies examining how high gravitational acceleration and anti-gravity maneuver affect cardiac function are rare. Purpose This study aimed to investigate the effect on cardiac function of fighter pilots exposed to high G acceleration forces. Methods From July 2019 to June 2020, fighter pilots underwent regular health check-up and echocardiogram. Data from trainees who did not experience a flight were used for control group. Echocardiographic data included left ventricle (LV) chamber size, systolic function, and diastolic function, as well as right ventricle systolic pressure (RVSP), inferior vena cava (IVC) collapsibility, and the tricuspid annular plane systolic excursion (TAPSE) to evaluate right heart function. The multiple linear regression analysis for TAPSE was performed with total flight time as an independent continuous variable in fighter pilots. Results We enrolled 29 fighter pilots (41±10years-old; male, 100%; mean flight time 1821±1186 hours) and 16 trainees (36±17years-old; male, 100%). There was no significant difference in age, blood pressure, LVEF, RVSP, and the IVC collapsibility between fight pilots and trainees. In multivariate linear regression analysis for fighter pilots, TAPSE was affected by total flight time with positive correlation. Conclusion The experience of fighter pilots exposed to the high G acceleration forces and anti-G maneuver might be associated with right heart function change, but not associated with the prominent cardiac structural changes.

Discretizing continuous variables in nutrition and obesity research: a practice that needs to be cut short

Background/objectivesNutrition and obesity researchers often dichotomize or discretize continuous independent variables to conduct an analysis of variance to examine group differences. We describe consequences associated with dichotomizing and discretizing continuous variables using two cross-sectional studies related to nutrition.Subjects/methodsStudy 1 investigated the effects of health literacy and nutrition knowledge on nutrition label accuracy (n = 612). Study 2 investigated the effects of cognitive restraint and BMI on fruit and vegetable (F/V) intake (n = 586). We compare analytic approaches where continuous independent variables were either discretized/dichotomized or analyzed as continuous variables.ResultsIn Study 1, dichotomization of health literacy and nutrition knowledge for 2 × 2 ANOVA revealed health literacy had an effect on nutrition label accuracy. Nutrition knowledge has an effect on nutrition label accuracy, but the health literacy by nutrition knowledge interaction was not significant. When analyzed using regression, the nutrition knowledge effect was significant. The simple effect of health literacy was also significant when health literacy equals zero. Finally, the quadratic effect of health literacy was negative and significant. In Study 2, dichotomization and discretization of cognitive restraint and BMI were used for three ANOVAs, which discretized BMI in three ways. For all ANOVAs, the BMI main effect for predicting fruit and vegetable intake was significant, the interaction between BMI and cognitive restraint was non-significant, and cognitive restraint was only significant when both variables were dichotomized. When analyzed using regression, the continuous mean-centered variables, and their interaction each significantly predicted F/V intake.ConclusionsDichotomizing continuous independent variables resulted in distortions of effect sizes across studies, an inability to assess the quadratic effect of health literacy, and an inability to detect the moderating effect of BMI. We discourage researchers from dichotomizing and discretizing continuous independent variables and instead use multiple regression to examine relationships between continuous independent and dependent variables.

Weekend and holiday admissions for decompensated heart failure and in-hospital mortality. A cumulative effect of “nonworking” days?

Introduction and objectives: The aim of this study was to analyze whether nonelective admissions in patients with heart failure (HF) on nonworking days (NWD) are associated with higher in-hospital mortality.Methods: We conducted a retrospective (2018-2019) observational study of episodes of nonelective admissions in patients aged 18 years and older discharged with a principal diagnosis of HF in acute general hospitals of the Spanish National Health System. NWD at admission were defined as Fridays after 14:00 hours, Saturdays, Sundays, and national and regional holidays. In-hospital mortality was analyzed with logistic regression models. The length of NWD was considered as an independent continuous variable. Propensity score matching was used as a sensitivity analysis.Results: We selected 235 281 episodes of nonelective HF admissions. When the NWD periods were included in the in-hospital mortality model, the increases in in-hospital mortality compared with weekday admission were as follows: 1 NWD day (OR, 1.11; 95%CI, 1.07-1.16); 2 days (OR, 1.13; 95%CI, 1.09-1.17); 3 (OR, 1.16; 95%CI, 1.05-1.27); and ≥ 4 days (OR, 1.20; 95%CI, 1.09-1.32). There was a statistically significant association between a linear increase in NWD and higher risk-adjusted in-hospital mortality (chi-square trend P = .0002). After propensity score matching, patients with HF admitted on NWD had higher in-hospital mortality than those admitted on weekdays (OR, 1.11; average treatment effect, 12.2% vs 11.1%; P < .001).Conclusions: We found an association between admissions for decompensated HF on an NWD and higher in-hospital mortality. The excess mortality is likely not explained by differences in severity. In this study, the “weekend effect” tended to increase as the NWD period became longer.

The Relationship between Exercise Capacity and Muscle Strength, Physical Activity, Fatigue and Quality of Life in Patients with Cancer Cachexia

Background Exercise capacity is a significant determinant of mortality for cancer patients, so knowing the possible determinants of exercise capacity will produce physical and psychological benefits for individuals with cancer cachexia. Purpose To investigate the relationship between exercise capacity on peripheric and respiratory muscle strength, physical activity, fatigue and quality of life in subjects with cancer cachexia. Methods The study included 31 patients diagnosed with cancer cachexia. Functional capacity was assessed by 6-Minute Walk Test, hand grip strength and proximal muscle mass by hand dynamometer, respiratory muscle strength by the Maximum Expiratory Pressure and Maximum Inspiratory Pressure measurements, physical activity by International Physical Activity Questionnaire Short Form, fatigue by Brief Fatigue Inventory, and quality of life by EORT-QLQ-C30. The relationship between functional capacity and continuous independent variables was determined using Spearman’s or Pearson’s tests. Results A strong positive correlation was observed between exercise capacity and expiratory muscle strength (r = 0.75, p < 0.001), activity level (r = 0.68, p < 0.001), and quality of life global health status (r = 0.74, p < 0.001). Conversely, a strong negative correlation was found between exercise capacity and fatigue severity (r = −0.64, p < 0.001). Conclusion Higher exercise capacity in cancer cachexia patients is linked to reduced fatigue, improved respiratory muscle strength, increased physical activity levels, and enhanced quality of life. When designing rehabilitation programs or exercise interventions for individuals with cancer cachexia, it is crucial to assess their exercise capacity and tailor the programs accordingly.

Test of bivariate independence based on angular probability integral transform with emphasis on circular-circular and circular-linear data

Abstract The probability integral transform of a continuous random variable X X with distribution function F X {F}_{X} is a uniformly distributed random variable U = F X ( X ) U={F}_{X}\left(X) . We define the angular probability integral transform (APIT) as θ U = 2 π U = 2 π F X ( X ) {\theta }_{U}=2\pi U=2\pi {F}_{X}\left(X) , which corresponds to a uniformly distributed angle on the unit circle. For circular (angular) random variables, the sum modulus 2 π \pi of absolutely continuous independent circular uniform random variables is a circular uniform random variable, that is, the circular uniform distribution is closed under summation modulus 2 π \pi , and it is a stable continuous distribution on the unit circle. If we consider the sum (difference) of the APITs of two random variables, X 1 {X}_{1} and X 2 {X}_{2} , and test for the circular uniformity of their sum (difference) modulus 2 π \pi , this is equivalent to test of independence of the original variables. In this study, we used a flexible family of nonnegative trigonometric sums (NNTS) circular distributions, which include the uniform circular distribution as a member of the family, to evaluate the power of the proposed independence test by generating samples from NNTS alternative distributions that could be at a closer proximity with respect to the circular uniform null distribution.

Machine Learning Based Prediction of Distant Metastasis in Rectal Cancer Patients with Complete Response following Neoadjuvant Chemoradiotherapy

Distant metastasis (DM) is the leading cause of disease recurrence in rectal cancer patients with clinical or pathological complete response (CR) following neoadjuvant chemoradiotherapy (nCRT). Identifying these patients may help to tailor the treatment approach. The aim of this study was to predict the rectal cancer patients with high risk of DM following CR with nCRT, using machine learning (ML), based on patient, disease and treatment related clinical factors. Data of the rectal cancer patients treated with nCRT at three academic centers between 2010-2022 were collected. Patients with secondary malignancies were excluded. 157 patients with CR were included in the analysis. Median age was 63 (31-85). Female/male ratio was 59/98. Tumor location was proximal-, mid- and distal-rectum in 9, 65 and 83 patients, respectively. cT stage was T2, T3 and T4 in 14, 120 and 23 patients, respectively. 122 patients had cN (+). Median tumor and regional lymph nodes doses were 50.4 (45-56) Gy and 45 (45-50.4) Gy, respectively, in 25-28 fractions, concurrently with capecitabine. 63 patients had clinical CR and were followed up without surgery, and 94 had pathological CR. Median RT-surgery interval was 11 (3.4-59) weeks. 41 patients were managed with total neoadjuvant treatment and 52 received adjuvant systemic treatment. Median follow up time was 37 (3-143) months. DM was detected in 18 patients at median 14 (2-87) months and local tumor recurrence/progression was seen in 9. A ML model was used to predict DM based on patient, disease and treatment related 37 clinical factors. Statistical analysis was conducted using Python (v.3.7). P<0.05 was accepted as statistically significant. Multiple Linear Regression was used to analyze the relationship between DM and independent continuous variables. Chi-square test was used to analyze the difference between the observed frequency distribution of a categorical variable and the expected frequency distribution. Confusion matrix algorithm was used to evaluate the performance of the model. Logistic regression algorithm (LRA) was selected as the best performing algorithm for the ML model that used 37 variables from 157 patients. The model was trained using 75% of the data and the remaining 25% was used for testing. CEA, ratio of post- to pre-RT tumor SUVmax, tumor dose, RT-surgery interval, pre-RT weight loss, smoking history, cN status, and gender were significant parameters to predict DM, based on the LRA of the ML model. Accuracy score of the model was 92% and AUC of the ROC was 74%. Precision (positive predictive value) and recall (sensitivity) of the model were 67% and 50%, respectively. Machine learning has a high rate of correctly classifying patients and good performance in predicting those with DM. Further studies with larger patient numbers are needed.

Do Peers, Neighborhood Disorder, Religiosity, Spirituality, and Family Support Influence Polysubstance Use Among Older Youth Transitioning from Foster Care?

ABSTRACT Purpose Few studies have explored polysubstance use among youths aging out of foster care, despite higher rates of substance misuse for youths exiting foster care than those in the general population. Polysubstance use has been linked to substance use disorders, health problems, cognitive impairment, suicide, and overdose. Method This study investigates understudied risk and protective factors associated with polysubstance use with data from 384 youth who turned 17 years old between December 1, 2001, and June 30, 2003, and were transitioning out of foster care from the Missouri Children’s Division. We conducted bivariate analyses with chi-square tests for categorical variables and Analysis of Variance with continuous independent variables. Then we conducted a multinomial logistic regression to explore differences between individuals who used 1 or no substances, individuals who used only alcohol and marijuana, and individuals who used 2 or more substances. Results Bivariate analyses found that being white, having deviant peers, and living in a more disordered neighborhood were risk factors for polysubstance use. Multinomial logistic regression results found that being white (RR = 6.89, p < .001), having deviant peers (RR = 1.15, p < .001), and living in a more disordered neighborhood (RR = 1.13, p < .05), increased the risk engaging in polysubstance use. Discussion Similar to findings in other studies, we found that deviant peers and neighborhood disorder increase the risk of polysubstance use, but family support, church attendance, and spirituality were not protective against polysubstance use. Conclusion Interventions should work to reduce deviant peer relationships among foster youth.

Constructing composite scores for contemporaneous behaviors: A comparison of four approaches

AbstractComposite scores, where the results of two or more measures are combined, are commonly used in many fields, including ethology. Composite scores can simplify the analysis and interpretation of data while capturing the salient features of the underlying latent variable(s) approximated by the score. Here we outline four approaches for constructing composite scores in ethological studies: ad hoc (AH) assignment, discriminant analysis (DA), principal components analysis (PCA), and partial least squares (PLS). We give examples of each using previously published data from a study of responses of lone star ticks (Amblyomma americanum) to several deterrent phytochemicals. In most cases, researchers construct AH composite scores by subjectively assigning weights and signs to the behavioral components; unity weighting constrains weights to −1 or 1 on standardized variables. Because the weights and signs of the coefficients are subjectively assigned, AH scores may generate a spurious result. DA can be used to construct composite behavioral scores when there are clearly defined treatments or preference tests using distinct stimuli. The DA score created consists of orthogonal variables that capture the variability in the behavioral measures most closely aligned with the differences among treatment or stimuli variables. This approach assumes that subjects discriminate treatment or stimuli differences, but may not manifest clear overt behavior that they are able to do so; it reduces dimensionality, usually to a single axis, representing the underlying latent variable of interest. The PCA approach is similar to DA except that the composite score is created independently of treatment or stimuli variables. Thus, this method can be used to investigate possible relationships between a composite score and any relevant independent variable, perhaps measured asynchronously with the behaviors. PLS is a multivariate method related to DA and PCA and is also used to create latent orthogonal variables. However, these new variables are constructed to maximize correlation with one or more continuous independent variables. Creation of a composite score requires the researcher to consider not only the method used to create it, but, at an earlier stage in the research, which behaviors should be components and how best to measure them.

ANALYSIS OF COACHING AND MENTORING TOWARDS EMPLOYEE PERFORMANCE AT LOTTE GROSIR MANADO

This study aims to analyze and find out if coaching and mentoring have an effect on Employee Performance at Lotte Grosir Manado’s employees. This research is using quantitative method. It will explain the relationship of variables through Causal analysis in the scale method between two continuous independent variables and one dependent variable. Researcher distributed questionnaires and the sample used was 30 respondents from fresh and bakery division. The result showed both coaching and mentoring simultaneously have a positive effect on Employee Performance at Lotte Grosir Manado. But coaching partially do not has a significant effect on employee performance, but mentoring has a significant on employee performance. Based on the results it can be said that Coaching and Mentoring have an important role in effecting Employee Performance Keywords: coaching, mentoring, employee performance

COVID-19 FEAR AND HEALTH-RELATED QUALITY OF LIFE OF OLDER ADULTS: EVIDENCE FROM IRAN

SARS-CoV-2 spread rapidly, infecting people around the globe including the Iranian population. Among those infected, the highest percentage in infection prevalence was amongstthose aged 60 years and older. The aim of the study was to examine Covid-19 fear and health-related quality of life in older adults.This cross-sectional study was conducted with a total of 205 elderly froman Iranian province.The research data were collected through an online questionnaire consisting of three parts: Personal Information Form, the Fear of COVID-19 Scale (FCS), and the World Health Organization Life Quality Scale-Short Form (WHOQOL-BREF).An independent two-group t-test (Student's t-test), and one-way ANOVA test were used to compare the means of the respective groups. The Pearson correlation coefficient was calculated to determine the level of correlation between two independent continuous variables. This data was analyzed using LISREL through confirmatory factor analysis.In total, 205 elderly people responded. Participants (n=205) were in the age range of 60-87 with an average age of 676.83 years in men and 60-92 with an average age of 666.60 in women.The mean score was obtained for FCS19.56.6, the mean score was obtained for the WHOQOL-BREF 61.219.3. The mean score of FCS in men (20.46.3) was significantly higher than the mean score in women (18.36.7) (p=0.023). It was found that there were very weak but significant inverse correlations between FCS and the WHOQOL-BREF (r = 0.22; p=0.001).In conclusion, it was found that fear of COVID-19 was below the medium level, while their WHOQOL-BREF was above the medium level.Finally, we also found that, as the score from FCS increased, the scores obtained from WHOQOL-BREF decreased.

Comparison of Hospitalization and Post-Hospitalization Outcomes among Family Medicine Hospitalists and Internal Medicine Hospitalists in a Tertiary Center: A Prospective Cohort Study

Background: There is a lack of studies comparing hospitalization and post-hospitalization outcomes between internal medicine (IM) hospitalists and family medicine (FM) hospitalists. &#x0D; Objective: To compare the length of stay (LOS), hospital cost, and 30-day all-cause readmission rate among patients treated by IM hospitalists and FM hospitalists. &#x0D; Design and Setting: Prospective cohort study in a referral center. Propensity score matching was used to balance baseline characteristics between comparative arms.&#x0D; Participants: 747 patients 18 years and older who were admitted to hospitalist services.&#x0D; Intervention: Treatment by IM hospitalists and FM hospitalists.&#x0D; Main Measures: LOS, hospital cost, and 30-day all-cause readmission. Treatment arms were compared by two methods. We compared patients who were seen by FM exclusively with those treated exclusively by IM services. Covariate adjusted differences in outcomes were estimated by multivariable regression. For a secondary set of analyses, exposure to FM and IM was converted to a continuous independent variable.&#x0D; Key Results: Forty, 333, and 374 patients were seen by FM, IM, and a combination of both services, respectively. Using average treatment on the treated as the estimand, FM care provided a shorter weight-adjusted LOS by 0.5 days (CI: -0.92- -0.04, P =0.026) compared to IM, but no difference in hospital cost (-126, CI: -906-653, P=.74). There was no difference in adjusted hazard for 30-day readmission between FM and IM (HR: 2, CI: 0.67-6.2, P =0.062). Propensity weight-adjusted multiple regression models of the complete cohort (n=747) did not show any difference in any outcomes with increased exposure to FM care. &#x0D; Conclusions: Understanding variation in practices and outcomes between different hospitalist models opens opportunities to improve care and decrease the length of stay.

Hand grip strength: A reliable assessment tool of frailty status on the person with type 2 diabetes mellitus

ObjectiveThe aim was to assess the influence of specific type 2 diabetes Mellitus-related features on the hand grip strength in a diabetic outpatient-based population. Material and methodsA cross-sectional population-based study was conducted. Seventy-three individuals were included after excluding cases of refusal to participate or medical concerns that had potential to affect dominant hand strength. A calibrated dynamometer was used to assess dominant hand grip strength. Two multiple linear regression models were used to predict mean hand grip strength values. Spearman and Pearson correlations were calculated for all possible pairwise combinations of significant independent continuous variables and mean hand grip strength values. ResultsReduced hand grip strength prevalence was 54.8%, from which 70.8% people had ≥ 65 years. Twenty four percent of individuals aged < 65 years showed decreased hand grip strength. Increasing age, female gender and type 2 diabetes Mellitus duration were significantly associated with reduced hand grip strength (P<0.001; P=0.017; P=0.001, respectively). For each 10 years of disease duration, individual mean hand grip strength decreased around 3kg (P=0.001). ConclusionsReduced hand grip strength prevalence was substantial in the geriatric subgroup and not negligible in younger patients with a contribution long-term Diabetes, increasing age and female gender as risk factors. This study displays a unique, practical and reliable approach to select at-risk patients who need close screening programs and clinical surveillance concerning frailty status.

Effects of multicollinearity on type I error rate and test power of binary logistic regression model: A simulation study

In this study, the effect of multicollinearity, which is defined as high correlation, on the type I error rate and test power of the binary logistic regression models were studied. To do this, one dependent variable that consists of 1 and 2 and four continuous independent variables that were randomly drawn from the standardized normal distribution were taken into consideration in the constructed binary logistic regression model. To calculate the type I error rates and test power, the simulation study was performed 100.000 times. The simulation study repeated for the sample sizes of 10, 20, 30 and 40 the various degrees of correlations, namely 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90%. In order to calculate test power, differences (δ) were created between population means by adding to the mean of the second population in standard deviation units as 0.5, 1.0, 1.5 and 2.0, respectively. The simulation runs exhibited that the increasing degree of multicollinearity among independent variables had no influence on type I error rates, provided that the sample size should not be smaller than 30. The power of the binary logistic regression was least affected by the increasing degrees of multicollinearity when the sample size is 10 and there is a 0.5δ difference between population means. The fact that there was a marked decline in test power with rising multicollinearity for all sample sizes clarified that the binary logistic regression was most powerful if there is no strong multicollinearity among independent variables when there is a 1.0δ difference between population means. The negative impact of the rising degrees of multicollinearity on the test power can be avoided if the number of observations is sufficiently large and if the populations were satisfactorily separated from each other.