Factors Affecting Balance in Inline Skating Athletes Based on Components of Single-Leg Stand, Tandem Stand, Age and Body Mass Index (BMI)

Introduction: Abnormal Body Mass Index (BMI), characterised by a higher percentage of fat mass, has notable effects on postural control, leading to a forward shift in posture that exceeds the Base Of Support (BOS) boundary due to increased segmental mass and a compromised ability to regain stability after a disruption caused by excess adiposity. Aim: To investigate the potential impact of BMI on the coordination, static balance and dynamic balance of young adults. Materials and Methods: The present case-control study was conducted in the Department of Physiotherapy, School of Healthcare and Allied Sciences (SoHAS), G D Goenka University, Gurugram, Haryana, India from November 2023 to April 2024. Study was conducted among 90 subjects from the Delhi-NCR region, aged between 18 years and 30 years and including both genders, were recruited. They were categorised into three groups based on Asian Pacific BMI classifications: 29 subjects in the normal weight group (BMI 18.5-22.9 kg/m2 ), 26 subjects in the overweight group (BMI 23-24.9 kg/m2 ), and 35 subjects in the obese group (BMI >25 kg/m2 ). Body composition, balance tests and coordination tests were assessed for all subjects. The p-value and F-values were calculated to assess group differences using the One-way Analysis of Variance (ANOVA) method, indicating significant results (p-value<0.01) for static and dynamic balance as well as coordination tests. Subsequently, post-hoc tests were conducted to explore specific differences among the groups. Results: The mean ages of the normal weight, overweight and obese groups were 22.10±2.38 years, 21.77±2.90 years and 21.91±2.38 years, respectively. The mean BMI of the normal weight, overweight and obese groups were 20.23±1.30 kg/ m2 , 23.99±0.68 kg/m2 and 29.69±3.09 kg/m2 , respectively. The ANOVA single factor test showed a significant difference between the normal weight, overweight and obese groups in the Single Leg Standing (SLS) test with opened and closed eyes on each leg for static balance; in the Timed Up and Go (TUG) test for dynamic balance; and in sidewalking, tandem walking, and heel walking for coordination at p-value<0.05. The posthoc test showed a significant difference in all the parameters for overweight and obese groups in comparison to the normal weight group at p-value<0.016. Conclusion: Abnormal BMI affects both static and dynamic balance along with coordination in young adults. Therefore, preventive measures should be considered to normalize BMI to prevent coordination and balance issues in overweight and obese young adults.

Obesity is one of the greatest public health challenges in the world with childhood prevalence rates between 20–26% and numerous associated health risks. The aim of the current study was to analyze the 11-year follow-up data of the Ulm Birth Cohort Study (UBCS), to identify whether abnormal eating behavior patterns, especially restrained eating, predict body mass index (BMI) at 11 years of age and to explore other factors known to be longitudinally associated with it. Of the original UBCS, n = 422 children (~ 40% of the original sample) and their parents participated in the 11-year follow-up. BMI at age 8 and 11 as well as information on restrained eating, psychological problems, depressive symptoms, lifestyle, and IQ at age 8 were assessed. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to predict children’s BMI scores at age 11. PLS-SEM explained 68% of the variance of BMI at age 11, with BMI at age 8 being the most important predictor. Restrained eating, via BMI at age 8 as well as parental BMI, had further weak associations with BMI at age 11; no other predictor was statistically significant. Since established overweight at age 8 already predicts BMI scores at age 11 longitudinally, obesity interventions should be implemented in early childhood.

<p>Increased prevalence of unhealthy lifestyles and dietary problems have been reported among the population especially female college students. This research aimed to test the model whether self-esteem, attitude towards body, eating habits, and Body Mass Index (BMI) affects cognitive reactivity in female college students. A total of 140 female university students aged 18-22 years participated in this study. Data analysis used Partial Least Square Structural Equation Modeling (PLS-SEM). A descriptive analysis using a one-way ANOVA test was performed to see whether there were differences among variables. Results show that self-esteem has a significant effect on body attitude and a negative significant effect on BMI and cognitive reactivity. Body attitude has a significant positive effect on eating habits. In contrast, it has a significant negative effect on BMI. The eating habit has a significant positive effect on cognitive reactivity. BMI has a significant effect on cognitive reactivity. Further, body attitude significantly mediates the relationship between self-esteem and eating habits. Also, body attitude significantly mediated the relationship of self-esteem with BMI. Additionally, the obese group has lower self-esteem and lower body attitude compared to normal and overweight groups.</p>

Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM)In view of its essential role in knowledge creation, multivariate data analysis prevails in the social sciences literature. The field of tourism is not an exception, specifically in the widely adoption of structural equation modeling (SEM), a multivariate technique, by tourism researchers over the past decade. While there are two major types of SEM including covariance-based SEM (CB-SEM) and variance-based SEM (PLS-SEM), the former dominated previous tourism research. However, increasing use of PLS-SEM in tourism research has been witnessed in recent years. This upward trend is likely to persist in the near future given the growing popularity of PLS-SEM in other social sciences domains like marketing, strategic management, and management information system, as specified in the preface of the book. Indeed, PLS-SEM, in relative to CB- SEM, provides more flexibility in handling of data. For instance, PLS-SEM is well-suited for accommodating small sample sizes and complex model, fortesting a model containing both formative and reflective constructs, and for handling single-item measures. To this end, the timely introduction of the book A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) helps tourism researchers stand at the front edge of the SEM technique and make effective use of the PLS-SEM in data analysis. Additionally, the book illustrates the application of PLS-SEM with a free downloadable software namely SmartPLS which is essential to extend the application of PLS-SEM in tourism research.Authored by Hair, Hult, Ringo, and Sarstedt, the book consists of eight chapters. To equip the readers with the basic knowledge of PLS- SEM, Chapter 1 delineates the meaning of SEM and its relationship with multivariate data analysis, followed by a description of the major elements in multivariate data analysis. Then the basic elements of PLS-SEM are explained. Finally, PLS-SEM is distinguished from its counterpart namely CB-SEM while the major characteristics of PLS-SEM and the conditions where the PLS-SEM are more adequate than CB-SEM and vice versa are discussed. To step in the application of PLS- SEM, Chapter 2 firstly explicates the concepts in structural model specification including mediation, moderation, and higher-order models. Then specification of measurement model is explained with a special focus on the differences between reflective and formative measures. After that, the issues that need to be addressed after data collection are discussed. The chapter ends by creating the model in the SmartPLS is illustrated. With an established model, Chapter 3 focuses on model estimation. The chapter explains the algorithm underpinning the estimation and the statistical properties of the PLS-SEM method, as well as the options and parameter settings for running the algorithm. Following that, the issues about interpretation of results are explained. The final section illustrates the execution of model estimation in the SmartPLS.Based on the model estimation, empirical measures of the measurement and structural models are derived, where evaluation of the models takes place. Chapter 4 exhibits the major steps in model evaluation in the beginning. Thereafter, the chapter explains the evaluation of reflective measurement models according to three major criteria including internal consistency reliability, convergent validity, and discriminant validity, followed by an illustration with the SmartPLS. Chapter 5 explains the assessment of formative measurement models with respect to the criteria of convergent validity, collinearity, and significance and relevance of the formative indicators. The chapter also elucidates the basic concepts of bootstrapping which is used to examine the statistical significance of estimates in PLS- SEM. An illustration of the assessment of formative measurement model in the SmartPLS follows. Chapter 6 continues the topic on model evaluation by focusing on the assessment of structural model. …

Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example

Health hazards of obesity have been recognized for centuries, appearing, for example, in writings attributed to Hippocrates. From the later decades of the 20th century through the present, there have been numerous epidemiological studies of the relationship between excess weight and the total, or all-cause, mortality rate,1 a critical cumulative measure of the public health impact of any health condition. Using body mass index (BMI), an indicator of relative weight for height (weight [kg]/height [m]2) and a frequently used surrogate for assessment of excess body fat, these studies have found linear, U-shaped, or J-shaped relationships between total mortality and BMI. That is, in some studies, both the thin and the obese were more likely to die than those in between. There is, however, always a point at which increasing BMI is associated with increasing mortality risk, but the BMI at which this occurs varies across studies and populations.2 Currently,3 overweight in adults is defined as a BMI of 25.0 to <30.0 kg/m2 and obesity as a BMI of ≥30.0 kg/m2 (Table 1). A number of studies have found no significant relationship between BMI in the overweight range and mortality rate4 and have shown the nadir of mortality risk to be in the overweight range. In particular, commentaries in both the lay press5–7 and scientific literature2,8,9 subsequent to recent reports from National Health and Nutrition Examination Surveys (NHANES)10,11 have highlighted the confusion and controversy regarding this issue. Some have interpreted the recent data to mean that overweight is not detrimental to health and is not in itself a public health concern and that drawing attention to the need for weight loss in this range will have negative effects on the health and well-being of the general population.8 Others have argued …

Partial least squares structural equation modelling (PLS-SEM) is becoming a popular statistical framework in many fields and disciplines of the social sciences. The main reason for this popularity is that PLS-SEM can be used to estimate models including latent variables, observed variables, or a combination of these. The popularity of PLS-SEM is predicted to increase even more as a result of the development of new and more robust estimation approaches, such as consistent PLS-SEM. The traditional and modern estimation methods for PLS-SEM are now readily facilitated by both open-source and commercial software packages. This book presents PLS-SEM as a useful practical statistical toolbox that can be used for estimating many different types of research models. In so doing, the authors provide the necessary technical prerequisites and theoretical treatment of various aspects of PLS-SEM prior to practical applications. What makes the book unique is the fact that it thoroughly explains and extensively uses comprehensive Stata (plssem) and R (cSEM and plspm) packages for carrying out PLS-SEM analysis. The book aims to help the reader understand the mechanics behind PLS-SEM as well as performing it for publication purposes. Features: Intuitive and technical explanations of PLS-SEM methods Complete explanations of Stata and R packages Lots of example applications of the methodology Detailed interpretation of software output Reporting of a PLS-SEM study Github repository for supplementary book material The book is primarily aimed at researchers and graduate students from statistics, social science, psychology, and other disciplines. Technical details have been moved from the main body of the text into appendices, but it would be useful if the reader has a solid background in linear regression analysis.

The pathological condition of obesity has an effect on quality of life. Larger body size, greater body mass index, abnormal body fat distribution, and the potential for neuromuscular disorders in obese children contribute to their body instability, resulting in poor static and dynamic stability. Poor body stability in obese adolescents can lead to a higher risk of falls and fractures. Daily physical activity and exercise are the main solutions to deal with balance problems in obese individuals. The exercise intervention that is often recommended for obese children and adolescents is endurance training, but the level of compliance is low because it takes a long time. Resistance training is an alternative that can be given but requires supervision to avoid the risk of injury. The purpose of this study was to compare the effects of endurance and resistance training on static and dynamic balance in obese adolescent boys. This study was conducted from July 2020 to September 2020. The subjects were adolescent boys aged 14-18 years with an age-appropriate body mass index (CDC BMI chart 2000) > P95. Twenty-five subjects were randomized into two groups, endurance and resistance groups. Frequency resistance training 3 times a week for 8 weeks. The intensity was determined using the Borg scale, gradually increasing. Endurance training starts with light intensity and gradually increases to moderate intensity with aerobic exercise (zumba fitness) for 30-60 minutes, a frequency of 3 times a week for 8 weeks. This study was conducted without calorie restriction. The assessments include static balance with the Standing Stork test and dynamic balance with the Modified BASS Balance test. The static balance (KS) in the pre-intervention endurance group was 2.21?0.80, and post-intervention 3.93?1.44, significance 0.002, while the pre-intervention resistance group was 2.30?0.67 and post-intervention 4.05?1.66 significance 0.001. The dynamic balance (KD) data for the pre-intervention endurance group was 79.08?11.1 and post-intervention 94.17?6.29, significance 0.000, while the pre-intervention resistance group was 75.85?14.88 and post-intervention 95.38?6.29, significance 0.000. Changes in static and dynamic balance before and after intervention between the 2 groups of endurance and resistance training there was no significant difference with a p-value of 0.955 for static balance and a p-value of 0.288 for dynamic balance. Endurance training and resistance training regularly 3 times a week for 8 weeks can have an effect on improving static and dynamic balance in obese adolescent boys.

The aim of this study was to assess if the evaluation of Body Mass Index is sufficient to define an overweight index in young athletes, or if a more effective evaluation is preferable in order to examine body fat mass, free-fat mass and hydration status in young athletes. Two hundred ninety-nine young athletes between the ages of 8 to 18 have been analyzed in this study. Data from evaluation in body composition of young athletes were studied and subdivided by age, sex and method used. In order to measure body composition in young people, the participants who attend our Department for sport eligibility examination, were evaluated through anthropometric measurements as far as, fat mass, fat-free mass and hydration status are concerned. The statistical differences showed with Body Mass Index and body fat assessment reflect that more accurate evaluation is preferable: the normal-weight with Body Mass Index are 78.0%, overweight 18.7% and obese 3.3% respect to a 75.0%, 14.0% and 11.0% detected with a body fat evaluation (P<0.000); statistical differences have been found also subdividing the group per sex, higher in males (P=0.046) than to females (P<0.000). Bio-impedance data shown a statistical differences in young obese athletes. The results obtained show clearly that the analysis of the Body Mass Index is not sufficient in young athletes. Therefore, for young athletes a full assessment of body composition would be appropriate to reduce classific-tion errors.

Introduction/Main Objectives: The traditional Partial Least Squares Structural Equation Modeling (PLS-SEM) method uses an ordinary least squares regression approach that assumes that indicators must have a continuous scale. When the indicators are categorical, the use of traditional PLS-SEM becomes less appropriate. Background Problems: Multidimensional poverty consists of dimensions that are measured by a binary scale. The use of binary PLS-SEM is better than traditional PLS-SEM in modeling the effect of dimensions on social protection on Java Island. Novelty: The use of binary PLS-SEM with factor scores from the item response theory model applied to the role of dimensions of multidimensional poverty to social protection has not been carried out yet. Research Methods: This study introduces binary PLS-SEM, which is modified from traditional PLS-SEM by changing the data input using a tetrachoric correlation matrix. Finding/Results: Empirical results show that the binary PLS-SEM measurement model is better than traditional PLS-SEM. Evaluation of the structural model shows that the path coefficients of binary PLS-SEM are better than traditional PLS-SEM. Both approaches have an overall model fit. The order of multidimensional poverty dimensions that affect social protection are education, living standard, and health.

With obesity on the rise among people living with HIV (PLWH), there is growing concern that weight gain may result as an undesired effect of antiretroviral therapy (ART). This analysis sought to assess the association between ART regimens and changes in body mass index (BMI) among ART-experienced, virologically suppressed PLWH. ART-experienced, virologically suppressed PLWH ≥18 years of age in the Observational Pharmacoepidemiology Research and Analysis (OPERA) cohort were included for analysis if prescribed a new regimen containing one of the following core agents: dolutegravir (DTG), elvitegravir/cobicistat (EVG/c), raltegravir (RAL), rilpivirine (RPV), or boosted darunavir (bDRV), for the first time between August 1, 2013 and December 31, 2017. Multivariable linear regression was used to assess the association between regimen and mean changes in BMI at 6, 12, and 24 months after switch. In unadjusted analyses, BMI increases ranged from 0.30 kg/m2 (bDRV) to 0.83 kg/m2 (RPV) at 24 months following switch, but gains were observed with every regimen. In adjusted analyses, compared to DTG, only bDRV was associated with a smaller increase in BMI at all time points, while EVG/c and RAL were associated with smaller increases in BMI at 6 months only. Overall, results were consistent in analyses stratified by baseline BMI category. BMI increases were relatively small but followed an upward trend over time in this cohort of treatment-experienced, suppressed PLWH. Gains were attenuated with a longer period of follow-up. BMI gains did not differ by regimens, except for bDRV regimens, which were consistently associated with smaller BMI increases than DTG.

Voluminous studies use Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data. One of the reasons for using PLS-SEM is when the structural model is complex. Studies employing complex structural models with many constructs and indicators lead to PLS-SEM selection for the analysis. The purposes of assessing the measurement model are to examine basic dimensions for construct variables, validate the dimensions, and determine the number of dimensions for each construct. Assessment of measurement model includes composite reliability and average variance extracted (AVE) to assess reliability and validity, respectively. This study tests the validity and reliability of the attitude construct in the context of compliance behavior of income zakat that other studies can use. This study assesses the measurement model to examine basic dimensions for construct variables, validate the dimensions, and determine the number of dimensions for each construct. Assessment of measurement model includes composite reliability and average variance extracted (AVE) to assess reliability and validity, respectively. This study hopes future research can adapt and adopts the attitude items used in this study in their future research.

Purpose: This study aimed to examine the reliability of the Timed Up and Go (TUG), Single Leg Standing (SLS), and 30-Second Sit-to-Stand (30STS) tests, both between different raters (inter-rater) and within the same rater (intra-rater), when administered through face-to-face and tele-evaluation methods in individuals with low back pain (LBP). Methods: Fifty individuals diagnosed with LBP and meeting the inclusion criteria participated in the study. Detailed demographic characteristics, including age, sex, and body mass index (BMI), were recorded. Functional tests were conducted both in a traditional face-to-face setting and under synchronous (real-time) and asynchronous (recorded) tele-evaluation conditions. Results: Inter-rater reliability between face-to-face and tele-evaluation methods was found to be very high (TUG: ICC=0.999; SLS: ICC=0.998; 30STS: ICC=0.996). Similarly, inter-rater reliability between two tele-evaluation sessions was also excellent (TUG: 0.997; SLS: 0.999; 30STS: 0.999). Intra-rater reliability, representing repeated measurements by the same rater, was also high in synchronous tele-evaluations, with ICC values of 0.997, 0.925, and 0.924 for TUG, SLS, and 30STS, respectively. Conclusions: The TUG, SLS, and 30STS tests demonstrated high reliability in tele-evaluation applications among individuals with LBP. These findings indicate that the tests are valid, feasible, and clinically useful tools for standardized, safe, and remote evaluation of functional capacity in a home environment.

Obesity Is Associated With Family History of ESRD in Incident Dialysis Patients

Research in international business and management (IM) is highly complex, contextual, and spans various subfields and related theoretical lenses. These characteristics pose research challenges that require utilizing sophisticated research designs and methodologies. This paper and our focused issue on ‘The use of partial least squares structural equation modeling (PLS-SEM) and complementary methods in IM research’ further clarify whether and how researchers using PLS-SEM can address (some of) the challenges in IM research. We explain how researchers can benefit from the (advanced) capabilities that PLS-SEM offers; either as a stand-alone method or in triangulation efforts that leverage complementary approaches. In addition, we review the IM literature for PLS-SEM applications and evaluate whether and how researchers are already using these approaches. We identify some room for improvement when it comes to the application of both more advanced PLS-SEM capabilities and the triangulation of PLS-SEM with qualitative data analyses, and techniques such as fuzzy set qualitative comparative analyses and necessary condition analyses. For better guidance, we refer to PLS-SEM application examples in which the methodological advances are used.