Class Of Variables Research Articles

Determining the Empathy Level of Coaching Education Students in the Sports Environment

The high level of empathy in the sports environment makes it clear that each individual in the sports factor displays a positive attitude in society and is positively influenced in terms of sporting success. This research was conducted to determine the level of empathic tendency of students in the Department of Coaching Education and to examine the effects of demographic differences on empathic tendency scores. The research group consisted of a total of 226 volunteers, 98 females and 128 males, studying in the Department of Coach Education, with a mean age of 21.66±2.26 years. The Empathy Scale in the Sports Environment was used in the study. Independent T-test, ANOVA and Pearson correlation test were used for the data collected in the study. According to the results of the study, there was a significant difference in the sub-dimension of emotional empathy in sports in relation to gender (p0.05). The significant difference was in favor of men. Although there was a significant difference between the groups when the total score of the scale was compared in the students' grade levels, no significant difference was found in multiple comparisons. A positive and significant relationship was found between sports age and emotional empathy in sports. These values show that emotional empathy in sport tends to increase with increasing sporting age. The findings demonstrate that the empathic tendencies of people who participate in sports are at a similar level regardless of gender, age and class variables, but as sports experience increases, so does the emotional empathy level. Applied training programs should be developed to improve empathy skills in sport science faculty and studies should be conducted to strengthen students' emotional and social skills in the sport environment.

Optimization of breeding program design through stochastic simulation with evolutionary algorithms.

The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing parameters such as the number of selected/phenotyped/genotyped individuals in the breeding program will impact genetic gain, genetic diversity, and costs. As a result, careful assessment and balancing of design parameters is crucial, taking into account the trade-offs between different breeding goals and associated costs. In a previous study, we optimized the resource allocation strategy in a dairy cattle breeding scheme via the combination of stochastic simulations and kernel regression, aiming to maximize a target function containing genetic gain and the inbreeding rate under a given budget. However, the high number of simulations required when using the proposed kernel regression method to optimize a breeding program with many parameters weakens the effectiveness of such a method. In this work, we are proposing an optimization framework that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization. The key idea is to consider a set of potential parameter settings of the breeding program, evaluate their performance based on stochastic simulations, and use these outputs to derive new parameter settings to test in an iterative procedure. The evolutionary algorithm was implemented in a Snakemake workflow management system to allow for efficient scaling on large distributed computing platforms. The algorithm achieved stabilization around the same optimum with a massively reduced number of simulations. Thereby, the incorporation of class variables and accounting for a higher number of parameters in the optimization framework leads to substantially reduced computing time and better scaling for the desired optimization of a breeding program.

Composition of Donkey Meat (Equus Asinus) from the Puno Region, Peru

Objective: To determine the proximate chemical composition of donkey meat (Equus asinus) considering the variables of animal class (young and adult) and commercial cut (shoulder, rib and leg) with the purpose of nutritionally assessing it as an alternative source of red meat. Theoretical Framework: Donkey meat is characterized by a low intramuscular fat content and could be used as a valid alternative to other species of red meat such as beef and sheep in the Puno region, Peru. However, little is known about the nutritional quality of meat from donkeys that are slaughtered at the main equine slaughterhouse in southern Peru. Method: Muscle tissue samples were taken from the following muscles: deltoid, Longissimus dorsi and biceps femoris representing the commercial cuts of shoulder, rib and leg, respectively. Chemical analysis (moisture, protein, fat and ash) was performed following the protocols established for meat by the Association of Official Analytical Chemists (AOAC), in the Bromatology Laboratory of the Faculty of Veterinary Medicine and Zootechnics of UNA-Puno. Data analysis was performed in a randomized complete block design (DBCA) and the LSD significance test (α=0.05). Results and Discussion: The proximate chemical composition of donkey meat is as follows: moisture 73.79%, protein 19.23%, fat 4.53% and ash 1.20%. The animal class (young and adult) and the commercial cut (shoulder, rib and leg) have a significant effect on the moisture and fat content (p≤0.05), with an inverse relationship between the two; but there is no effect on the protein and ash content (p>0.05). Implications of the research: It was possible to demonstrate that the meat of donkeys from the Puno region has a similar composition to the red meat of other domestic species, so it can be a good alternative to replace red meat. Originality/Value: This study contributes to the literature by knowing the chemical composition of donkey meat from the Puno region, Peru for consumption purposes as a substitute for red meat or for the preparation of meat by-products.

Cardiometabolic risk factor clusters in older adults using latent class analysis on the Bushehr elderly health program

Metabolic syndrome (MetS), comprising obesity, insulin resistance, hypertension, and dyslipidemia, increases the risk of type II diabetes mellitus and cardiovascular disease. This study aimed to identify the prevalence and determinants of specific clusters of the MetS components and tobacco consumption among older adults in Iran. The current study was conducted in the second stage of the Bushehr Elderly Health (BEH) program in southern Iran—a population-based cohort including 2424 subjects aged ≥ 60 years. Latent class analysis (LCA) was used to identify MetS and tobacco consumption patterns. Multinomial logistic regression was conducted to investigate factors associated with each MetS class, including sociodemographic and behavioral variables. Out of 2424 individuals, the overall percentage of people with one or more components of MetS or current tobacco use was 57.8% and 20.8%, respectively. The mean (SD) age of all participants was 69.3(6.4) years. LCA ascertained the presence of four latent classes: class 1 (“low risk”; with a prevalence of 35.3%), class 2 (“MetS with medication-controlled diabetes”; 11.1%), class 3 (“high risk of MetS and associated medication use”; 27.1%), and class 4 (“central obesity and treated hypertension”; 26.4%). Compared to participants with a body mass index (BMI) < 30, participants with BMI ≥ 30 were more likely to belong to class 3 (OR 1.91, 95% CI 1.31–2.79) and class 4 (OR 1.49, 95% CI 1.06–2.08). Polypharmacy was associated with membership in class 2 (OR 2.07, 95% CI 1.12–3.81), class 3 (OR 9.77, 95% CI 6.12–15.59), and class 4 (OR 1.76, 95% CI 1.07–2.91). The elevated triglyceride-glucose index was associated with membership in class 2 (OR 12.33, 95% CI 7.75–19.61) and class 3 (OR 12.04, 95% CI 8.31–17.45). Individuals with poor self-related health were more likely to belong to class 3 (OR 1.43; 95% CI 1.08–1.93). Four classes were identified among older adults in Iran with distinct patterns of cardiometabolic risk factors. Segmenting elderly individuals into these cardiometabolic categories has the potential to enhance the monitoring and management of cardiometabolic risk factors. This strategy may help reduce the severe outcomes of metabolic syndrome in this susceptible population.

Implementation of Problem Based Learning Model to Improve the Ability to Solve SPLDV in Grade 9 Students

This research focuses on the challenges in learning mathematics, especially in understanding and solving the system of linear equations of two variables, which is often an obstacle for students. This study aims to improve students' ability to solve SPLDV as well as increase students' motivation and engagement in learning. This study analyzed the effectiveness of Problem Based Learning (PBL) model in improving the ability to solve the System of Linear Equations of Two Variables (SPLDV) in class VIII students of SMPN 54 Surabaya. This study involved 34 students consisting of 18 male students and 16 female students using a Classroom Action Research (PTK) approach carried out in two cycles, each of which consisted of planning, action implementation, observation, and reflection stages. The instruments used were learning outcome tests, observation sheets, and student questionnaires. The results showed a significant increase in students' SPLDV problem solving ability, with the average score of students increasing from 65 in the pretest to 75 in the first cycle post-test, and reached 85 in the second cycle post-test. The proportion of students who met the Minimum Completion Criteria (KKM) increased from 50% in the first cycle to 85% in the second cycle. Further analysis showed that female students outperformed male students, with an average score of 87 and 82. Student engagement also increased, reaching 90% in the second cycle. This study concludes that the PBL model effectively improved SPLDV problem solving skills in grade 9 students and recommends its wider application in mathematics education.

A Socio- Stylistic Study of the Role of Social Identity in Selected Feminist Texts

The current study explores the relationship between social identity and stylistic features in the context of contemporary feminism with reference to gender, race, and class variables. Although many studies have been conducted concerning the concept of feminist language, the relationship between social identity and linguistic features has not received enough attention, which is why the present study aims to identify the specific strategies that are used to address the goals of feminism. This research seeks to fill this gap through an analysis of twenty four contemporary Feminist texts in articles, speeches and social media posts. Employing socio-stylistic eclectic approach, the research analyses the patterns of stylistic elements in various media contexts and analyses how such elements work with the audience as well as embody different social roles. The results of the study advance the theoretical macro-socio-linguistic paradigm of socio-stylistics by identifying the link between language and social status. In addition, the study is helpful for the leaders of feminism to know how language can be utilized in a constructive manner to advance the cause of feminism.

Ammonothermal Synthesis of Luminescent Imidonitridophosphate Ba4P4N8(NH)2:Eu2.

The structural variability of a compound class is an important criterion for the research into phosphor host lattices for phosphor-converted light-emitting diodes (pc-LEDs). Especially, nitridophosphates and the related class of imidonitridophosphates are promising candidates. Recently, the ammonothermal approach has opened a systematic access to this substance class with larger sample quantities. We present the successful ammonothermal synthesis of the imidonitridophosphate Ba4P4N8(NH)2:Eu2+. Its crystal structure is solved by X-ray diffraction and it crystallizes in space group Cc (no.9) with lattice parameters a=12.5250(3), b=12.5566(4), c=7.3882(2)Å and β=102.9793(10)°. For the first time, adamantane-type (imido)nitridophosphate anions [P4N8(NH)2]8- are observed next to metal ions other than alkali metals in a compound. The presence of imide groups in the structure and the identification of preferred positions for the hydrogen atoms are performed using a combination of quantum chemical calculations, Fourier-transform infrared, and solid-state NMR spectroscopy. Eu2+ doped samples exhibit cyan emission (λmax=498nm, fwhm=50nm/1981cm-1) when excited with ultraviolet light with an impressive internal quantum efficiency (IQE) of 41%, which represents the first benchmark for imidonitridophosphates and is promising for potential industrial application of this compound class.

Ternary blended concrete strength evaluation: experimental and artificial intelligence techniques

Purpose The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models. Design/methodology/approach DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set. Findings The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R2 values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively. Originality/value To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.

Robust Stochastic Model Predictive Control for Autonomous Vehicle Motion Planning

This work presents a Robust Stochastic Model Predictive Control (RSMPC) framework for real-time motion planning autonomous vehicles, addressing the complex multi-modal vehicle interactions. The proposed framework involves adding expert policy from observations to the dataset and applying the Data Aggregation (DAgger) method to filter unsafe demonstrations and resolve expert conflicts. A Dual-Stage Attention-based Recurrent Neural Network (DA-RNN) model is integrated to predict dual class variables from the dataset, producing a set containing constraints collision-avoidance predicted to be active. The RSMPC framework enhances formulation optimization by eliminating irrelevant collision avoidance constraints, resulting in faster control signals. The framework is applied iteratively, continuously updating observations and solving the RSMPC optimization formulation in real-time. Evaluation of the DA-RNN model achieved a recall value of 0.97 and a high accuracy rate of 98.1% in predicting dual interactions, with a minimal false negative rate of 0.026, highlighting its effectiveness in capturing interaction intricacies. Validated through simulations of interactive traffic intersections, the proposed framework demonstrably excels, showing high feasibility of 99.84% and a 15-fold increase in response speed compared to the baseline. This approach ensures autonomous vehicles navigate safely and efficiently in complex traffic scenarios, paving the way for more reliable and scalable autonomous driving solutions.

Toward a dynamic national transportation noise map: Modeling temporal variability of spectral traffic noise emission levels.

The National Transportation Noise Map predicts time-averaged road traffic noise across the continental United States (CONUS) based on annual average daily traffic counts. However, traffic noise can vary greatly with time. This paper outlines a method for predicting nationwide hourly varying source traffic sound emissions called the Vehicular Reduced-Order Observation-based Model (VROOM). The method incorporates three models that predict temporal variability of traffic volume, predict temporal variability of different traffic classes, and use Traffic Noise Model (TNM) 3.0 equations to give traffic noise emission levels based on vehicle numbers and class mix. Location-specific features are used to predict average class mix across CONUS. VROOM then incorporates dynamic traffic class mix data to obtain dynamic traffic class mix. TNM 3.0 equations then give estimated equivalent sound level emission spectra near roads with up to hourly resolution. Important temporal traffic noise characteristics are modeled, including diurnal traffic patterns, rush hours in urban locations, and weekly and yearly variation. Examples of the temporal variability are depicted and possible types of uncertainties are identified. Altogether, VROOM can be used to map national transportation noise with temporal and spectral variability.

Association of LSMx and LIMd Angles With Cephalometric Dental and Soft Tissue Parameters of Incisor Proclination and Lip Protrusion.

Soft tissue analysis can be used to assess anatomical features but may or may not accurately correlate with underlying hard tissue morphology, leading to an incorrect perception of malocclusion. This study aimed to assess the reliability of different soft tissue reference lines used to evaluate anteroposterior lip position and the position of incisors and malocclusion and compare it with those assessed via hard tissue angles (LSMx and LIMd) and determine if they are true indicators of underlying protrusion of incisors and malocclusion. A total of 120 pre-treatment lateral cephalometric radiographs were selected where patients were 18-30 years old, diagnosed as Skeletal Class I, II (Division 1 and 2), and III malocclusion. The measurements taken were SN to point A angle (SNA), SN to point B angle (SNB), angle between point A and point B (ANB), upper incisor to SN plane angle (UI-SN), upper incisor to palatal plane angle (UI-PP), incisor mandibular plane angle (IMPA), Ricketts' E line, Sushner's S2 line, nasolabial (NL) angle, mentolabial (ML) angle, LSMx angle, and LIMd angle. In the Class I malocclusion group, when the upper lip was assessed, the distribution of UI-SN, UI-PP, E line to UL, S line to UL, NL angle, and LMax was significantly different statistically (p=0.000), though the assessment of lower variables in Class I malocclusion showed the distribution of IMPA, E line to UL, S line to UL, ML angle, and LMand angle has a statistically significant difference (p=0.007). In Class II Division 2 malocclusion, a significant difference was observed for the upper variables (p=0.000), whereas the distribution of lower values was the same across all the variables (p=0.0724). In the sample of Class III malocclusion, a significant correlation was found in the upper variables, while the distribution among lower variables did not show any significant difference (p=0.211). This study indicates that the upper and lower soft tissue correlation with hard tissue variables is reliable for some variables but not throughout for all. Soft tissue analysis (under study) can be used to assess disproportion, but it fails to correlate to the underlying hard tissue morphology and does not explain the correct malocclusion. Further studies based on 3D diagnosis to formulate a close relationship are encouraged that can help assess soft and hard tissue patterns consistent with one another.

Statistical analysis of the effect of socio-political factors on individual life satisfaction

Life satisfaction refers to an individual’s cognitive evaluation of the quality of their life. The aim of the present study is to develop the current understanding of how perceived corruption, attitudes toward migration, perceived security, and strength of national identity influence life satisfaction. Additionally, the study examines how demographic variables of relationship status, social class, sex, religious affiliation, and country impact life satisfaction in the provided cultural context. Ordinal logistic regression analysis, Confirmatory Factor Analysis, and Structural Equation Modeling are used to analyze data from the World Values Survey. Findings from the analyses indicate that perceived corruption, perceived security, and strength of national identity have a significant impact on life satisfaction, whereas migration has an indirect effect on life satisfaction through perceived security. The present research can develop our current understanding of life satisfaction from a socio-political perspective.

A meta-analysis of the attitudes of university students in Türkiye toward ageism

ABSTRACT In today’s society, the issues surrounding aging, older adults, and ageism have gained considerable attention due to the rapidly increasing aging population. This study examines attitudes of the university students in Türkiye toward ageism. Employing a meta-analysis approach, the study investigates whether various sociodemographic factors affecting university students’ attitudes toward ageism. Utilizing Comprehensive Meta Analysis (CMA) software, the research examines potential disparities in attitudes toward ageism based on gender, class, age, family type, mother’s education level, father’s education level and the place where they spend most of their lives. The analysis involves evaluating Hedges’s g effect coefficient, Z statistic, and p-values to determine the relationship between ageism and demographic variables. To address publication bias, Egger’s regression intercept, Duval and Tweedie’s trim and fill, Rosenthal’s classic fail-safe N, and Orwin’s fail-safe N tests are conducted. Conclusively, the study finds that sociodemographic variables have a predominantly ‘very low’ or ‘low’ impact on ageism attitudes among university students. Specifically, only the variable of class shows a marginal effect (measured at −0.375), albeit statistically insignificant. Due to the absence of prior meta-analyses exploring the effects of sociodemographic factors on ageism among university students, these results cannot be directly compared with the existing research.

Incorporating Individual Differences in Conjoint Analysis: A Structural Equation Modeling Approach

In the SEMWISE (Structural Equation Modeling for Within-Subject Experiments) framework, traditional conjoint analysis is treated as repeated measurements, which facilitates the incorporation of individual differences through structural equation modeling. This approach allows for the use of goodness-of-fit indices to assess data-model consistency and to test assumptions in conjoint analysis, thus extending the analysis to a broader framework. This paper advocates for the introduction of latent class variables within this framework to conduct market segmentation. An empirical dataset analysis, performed using lavaan and Mplus, demonstrates how to apply SEM to conjoint analysis, effectively leveraging individual differences for market segmentation and associating personal characteristics with market segments. This progression through increasingly complex models also illustrates a workflow for researchers using SEM in conjoint analysis studies.

Leveraging multi-site electronic health data for characterization of subtypes: a pilot study of dementia in the N3C Clinical Tenant.

To provide a foundational methodology for differentiating comorbidity patterns in subphenotypes through investigation of a multi-site dementia patient dataset. Employing the National Clinical Cohort Collaborative Tenant Pilot (N3C Clinical) dataset, our approach integrates machine learning algorithms-logistic regression and eXtreme Gradient Boosting (XGBoost)-with a diagnostic hierarchical model for nuanced classification of dementia subtypes based on comorbidities and gender. The methodology is enhanced by multi-site EHR data, implementing a hybrid sampling strategy combining 65% Synthetic Minority Over-sampling Technique (SMOTE), 35% Random Under-Sampling (RUS), and Tomek Links for class imbalance. The hierarchical model further refines the analysis, allowing for layered understanding of disease patterns. The study identified significant comorbidity patterns associated with diagnosis of Alzheimer's, Vascular, and Lewy Body dementia subtypes. The classification models achieved accuracies up to 69% for Alzheimer's/Vascular dementia and highlighted challenges in distinguishing Dementia with Lewy Bodies. The hierarchical model elucidates the complexity of diagnosing Dementia with Lewy Bodies and reveals the potential impact of regional clinical practices on dementia classification. Our methodology underscores the importance of leveraging multi-site datasets and tailored sampling techniques for dementia research. This framework holds promise for extending to other disease subtypes, offering a pathway to more nuanced and generalizable insights into dementia and its complex interplay with comorbid conditions. This study underscores the critical role of multi-site data analyzes in understanding the relationship between comorbidities and disease subtypes. By utilizing diverse healthcare data, we emphasize the need to consider site-specific differences in clinical practices and patient demographics. Despite challenges like class imbalance and variability in EHR data, our findings highlight the essential contribution of multi-site data to developing accurate and generalizable models for disease classification.

Pengaruh Penggunaan Media Komik Terhadap Minat Baca Siswa di SD 1 Menawan

The aim of this research is to determine the effect of using comic media on students' reading interest at SD 1 Menawan. This research uses quantitative methods with the research method being Quasi Experiment (quasi experiment). Data collection techniques used in this research include interview guides, comics and questionnaires. The respondents in this study were all students in grades IV and V as the control and experimental classes at SD 1 Menawan. Data analysis techniques were processed and analyzed using SPSS 20 data processing software. The research data obtained was carried out by normality test, hypothesis test, and t-test. The sig value was obtained from the results of the t-test of the two variables of the experimental class and the control class of 0.000 &lt; 0.05, it can be concluded that Ho was rejected, which means that the use of comic media as a teaching material has an effect on the increase in students' interest in reading at SD 1 Menawan.

Fair Feature Selection: A Causal Perspective

Fair feature selection for classification decision tasks has recently garnered significant attention from researchers. However, existing fair feature selection algorithms fall short of providing a full explanation of the causal relationship between features and sensitive attributes, potentially impacting the accuracy of fair feature identification. To address this issue, we propose a fair causal feature selection algorithm, called FairCFS . Specifically, FairCFS constructs a localized causal graph that identifies the Markov blankets of class and sensitive variables, to block the transmission of sensitive information for selecting fair causal features. Extensive experiments on seven public real-world datasets validate that FairCFS has accuracy comparable to eight state-of-the-art feature selection algorithms while presenting more superior fairness.

Performance of an artificial intelligence/machine learning model designed to identify hospitalized patients with cancer who could benefit from timely specialized palliative care delivery.

1559 Background: Timely access to specialty Palliative Care (SPC) services provides significant benefits to hospitalized patients with cancer, including improvements in quality of life. However, many patients with cancer who could benefit from SPC suffer from lack of timely referral or do not receive SPC services. A previously published trial of an artificial intelligence/machine learning (AI/ML) model to predict need for SPC at Mayo Clinic increased timely SPC consultation of hospitalized patients overall. We sought to review the performance of this algorithm in patients with cancer across an expanded hospitalized population. Methods: The study population consisted of all patients admitted into a Mayo Clinic hospital in Minnesota, Wisconsin, Arizona and Florida between January 2020 and September 2023. Due to the size of the cohort a case-control sample of three controls for every case was created. Patients were considered to be a patient with cancer if they had at least one billing diagnosis up to 1 year prior to their index hospitalization from any of the 5 HCC categories denoting cancer: Metastatic Cancer and Acute Leukemia (HCC 8); Lung and Other Severe Cancers (HCC 9); Lymphoma and Other Cancers (HCC 10); Colorectal, Bladder, and Other Cancers (HCC 11); and Breast, Prostate, and Other Cancers and Tumors (HCC 12). The training data set consisted of 107,076 patient encounters with a total of 8,355,090 time periods of constant risk. An AI/ML model using gradient boosting methods which contained 269 variables (both static and time-varying) of various classes with SPC consultation treated as a time-to-event outcome was trained. Results: Due to the longitudinal nature of the prediction, performance was assessed using the max score AUC (using the max score a patient received during a given encounter prior to an event or discharge) to produce the AUC. The model had an overall AUC of 0.932 (0.929, 0.935 – 95% CI). We saw a decrease in performance of the AI/ML model in the Oncology population with an overall performance of 0.818 (0.806, 0.83). Some of the most influential variables were previous SPC visit, age, metastatic disease, acute leukemia diagnosis, and pain scores. Conclusions: An AI/ML model can effectively predict the need for an inpatient PC consult in a population of hospitalized patients with cancer. [Table: see text]

Heritability of class and status: Implications for sociological theory and research

Most individual-level outcomes of interest to sociologists are indirectly influenced by genetics, including socioeconomic outcomes like education, income, wealth, and occupational status. Despite this knowledge, an integration of sociological theories of class and status with the research literature on genetic heritability is largely lacking, and no previous studies have investigated the role of genetics specifically for class attainment – a central concept in sociology. To amend this, we estimate how much variation in such positions can be attributed to genetic and environmental factors in roughly 5000 Norwegian twin pairs. The results suggest that the variability in class and status attainment is influenced by genetics to a non-negligible degree, while shared environmental factors play a modest role as well. This is in line with previous findings using genetically informed designs to study other socioeconomic outcomes. Our study suggests that in addition to social environments, variation in class and status attainment can partly be explained by genetics.

Association between modifiable lifestyle pattern and periodontitis: a cross-sectional study based on NHANES

BackgroundPeriodontitis can be avoided with a healthy lifestyle. However, studies have only looked at one lifestyle, ignoring the connection between lifestyle patterns and periodontitis. The purpose of this study was to look at the association between modifiable lifestyle patterns and periodontitis.MethodsData were obtained from the National Health and Nutrition Examination Survey in 2009–2010 and 2011–2012. Smoke, drink, exercise, sleep duration, oral exams, and self-rated oral health were all lifestyle factors. The CDC/AAP classification/case definition was used to evaluate periodontitis. Drawing upon latent class analysis, distinct patterns of lifestyle were identified, with each participant exclusively affiliated with a single pattern. The association between lifestyle classes and periodontitis was then examined using ordinal logistic regression.Results4686 (52%) of the total 9034 participants, with a mean age of 54.08, were women. Three lifestyle latent classes were found by fitting 2–10 models: “Class 1” (52%), " Class 2” (13%), and " Class 3” (35%). The “Class 1” displayed a prevalence of oral examination (75%), favorable self-rated oral health (92%), and engagement in physical activity (50%). The ‘Class 2’ exhibited the lowest alcohol consumption (64%) and smoking rates (73%) but the highest prevalence of physical inactivity (98%). The ‘Class 3’ showed a tendency for smoking (72%), alcohol consumption (78%), shorter sleep duration (50%), absence of oral examinations (75%), and suboptimal self-rated oral health (68%). The influencing variables for the latent classes of lifestyle were age, education, and poverty level. Periodontitis risk may rise by 24% for each additional unhealthy lifestyle practiced by participants (OR = 1.24, 95% CI: 1.18–1.31). The ‘Class 3’ (OR = 1.80, 95% CI: 1.52–2.13) had a greater risk of periodontitis compared to the ‘Class 1’.ConclusionsOur analysis revealed that unhealthy lifestyle patterns are associated with periodontitis. These different lifestyle patterns need to be taken into account when developing public health interventions and clinical care.