Standard Class Research Articles

Telehealth based parental support over 6 months improves physical activity and sleep quality in children with autism: a randomized controlled trial

PurposeSleep disturbances are prevalent in autistic children. The emergence of telehealth offers new possibilities for remote professional intervention. By combining telehealth with parental support, this study aims to explore a novel family-based model to enhance moderate-to-vigorous physical activity (MVPA) and improve sleep quality in children with autism.MethodsThirty-four autistic children (mean age = 15.7 years) were randomly assigned to either a 6-month intervention group or a control group. Both groups received standard physical education classes at school. The intervention group received additional after-school telehealth support. MVPA and sleep quality were assessed 1 week before the intervention and at the 6-month follow-up.ResultsAfter 6 months, children in the intervention group nearly doubled their daily MVPA compared to the control group (Cohen's d = 8.34, CI95% = 6.17–10.52). Actigraphy-assessed sleep efficiency was notably higher (d = 2.35, CI95% = 1.44–3.26), and there were reductions in wake time (d = 1.65, CI95% = 0.84–2.46), sleep fragmentation (d = 0.80, CI95% = 0.07–1.52), and sleep latency (d = 0.82, CI95% = 0.09–1.54) were all reduced. These improvements in objective sleep metrics were corroborated by subjective assessments using the Sleep Disturbance Scale for Children (d = 0.86, CI95% = 0.13–1.59).ConclusionsTelehealth combined with parental support addresses barriers to enhancing health behaviors at home. This innovative model not only improves after-school MVPA and sleep quality in autistic children but also holds significant potential for benefiting other populations requiring remote support.Clinical Trial Registrationhttps://clinicaltrials.gov/study/NCT06444659?id=NCT06444659&rank=1 (NCT06444659).

Study protocol of a breathing and relaxation intervention included in antenatal education: A randomised controlled trial (BreLax study).

Antenatal education is part of antenatal care in many countries. Physical and mental preparation for childbirth and information on pain management are considered key elements of antenatal education classes. Evidence suggests that women who participate in antenatal education classes appear to benefit in terms of self-efficacy and childbirth experience. In particular, women with increased fear of childbirth benefit from trained breathing and relaxation techniques in antenatal education. However, little is known about the effect of breathing and relaxation techniques on the outcomes of healthy pregnant women without fear of childbirth or who do not have medical or obstetric risks, or on neonatal outcomes. The aim of this study is to test whether including a breathing and relaxation technique in an antenatal education class will improve self-efficacy towards birth compared to a standard antenatal education class. The study will be a two-armed randomised controlled trial (RCT). Healthy pregnant women between the 12th and 30th week of gestation with singleton low-risk pregnancies and who are receiving routine antenatal care will be recruited in a hospital in Switzerland. All women included will attend an 8-hour antenatal education class. The intervention group will additionally practise a breathing and relaxation technique, receive a handbook to guide their exercise practice at home, and be provided with access to an online brochure with video and audio recordings for guidance. Data on maternal and neonatal outcomes will be collected after recruitment, in the 37th week of pregnancy, and two to four weeks postpartum. The effectiveness of including a breathing and relaxation technique in antenatal education classes on women's self-efficacy is discussed as a means to improving women's pregnancy and childbirth outcomes. NCT06003946, SNCTP000005672.

THE MINIMUM ECCENTRIC-DOMINATING ENERGY OF A GRAPH

Let be a simple graph. A subset of vertices in is said to be an eccentric-dominating set if for each vertex not in , there exists at least one eccentric vertex in and . The cardinality of the minimum eccentric-dominating set is called the eccentric domination number, denoted by . In this article, we define and study the minimum eccentric-dominating energy , and compute the exact value for some standard classes of graphs. Also, we establish some bounds for .

Shaping the Bilingualism of Game-Addicted Players

In today's world, many people who spend more and more time in front of their computer and treat it as something more than a popular pass-time activity are often referred to being game addicted. At the same time, however, many of them, because of being exposed to different forms of dialogic English may be observed to excel the language and react to quite many situations they are exposed to both during the play and out of it. This study is meant to show how gaming addicts shape their bilingualism. Due to our research, it was found that such people who are truly addicted to games often reach a higher level of English much faster when contrasted to average L2 users. The research was based on the surveys filled by 54 carefully selected Internet respondents that fit the definition of a game addicted person (APA, 2022) and a standardized CEFR test they were requested to complete; the same test was also requested to be completed by the people who attend standardized blended L2 classes and who play similar games sporadically for pleasure. Consequently, a correlation between addicted and non-addicted people, and their CEFR test results was carried out. What followed was an analysis of the research results and the their detailed description.

Double domination number of graphs generated from unary products

A subset $S$ of $V(G)$ is a double dominating set of a graph $G$ if $S$ dominates every vertex of $G$ at least twice. The minimum cardinality of a double dominating set denoted by $\gamma_{2\times}(G)$, is the double domination number of $G$. In this paper, we identified the double domination number of graphs generated by applying various unary operations on standard graph classes.

The Dustiest Galactic S Stars: Mid-infrared Spectra from SOFIA/FORCAST

We present spectra of 12 of the reddest, and hence dustiest, S stars in the Milky Way, observed with the FORCAST grisms on SOFIA. S stars are asymptotic giant branch (AGB) stars with C/O ∼ 1, so their molecular and dust chemistries are dominated by neither O nor C, often leading to atypical spectral features from their molecules and dust grains. All of the stars in our sample have strong dust emission features at 10–11 μm, but the shape of the feature in most of the stars differs from the shapes commonly observed in either oxygen-rich or carbon-rich AGB stars. Two stars also show the 13 μm feature associated with crystalline alumina. Two have a water absorption band at ∼6.5–7.5 μm, and a third has a tentative detection, but only one of these three has the more common SiO absorption band at 7.5 μm. Three others show a red 6.3 μm emission feature from complex hydrocarbons consistent with “Class C” objects, and in a fourth it appears at 6.37 μm, redder than even the standard Class C hydrocarbon feature. Class C spectra typically indicate complex hydrocarbons that have been less processed by UV radiation, resulting in more aliphatic bonds relative to aromatic bonds. None of the S stars show a strong 11.3 μm hydrocarbon feature, which is also consistent with the presence of aliphatic hydrocarbons.

Intrusion Detection System Using Chaotic Walrus Optimization-based Convolutional Echo State Networks for IoT-assisted Wireless Sensor Networks

Wireless Sensor Networks (WSN) based Internet of Things (IoT) networks have achieved greater research interest due to their multi-purpose data collection and transmission over different geographical locations. However, the fabrication of different cyber-attacks in these networks has been a severe concern for applications such as remote healthcare, military communications, etc. These attacks question the integrity and security of WSN-IOT networks for such applications, and the traditional Intrusion Detection Systems (IDS) have shown increased false alarm rates because of their substantial processing overhead, resource constraints, and low detection rates. This paper presents an intelligent IDS model using Chaotic Walrus Optimization-based Convolutional Echo State Networks (CWO-CESN) to solve existing problems. It increases the detection accuracy of different attacks. In this CWO-CESN-based IDS model, the data are gathered from the sensor/IoT nodes and are pre-processed. Then, the proposed CWO-CESN learns the features from these data and classifies them into attacks and standard data classes. This proposed CWO-CESN is a hybrid classifier model that integrates Convolutional Neural Networks (CNN) and Echo State Networks (ESN) as a single model and employs Chaotic map-based population initialized Walrus Optimizer for optimizing the hyperparameters. Validated on benchmark datasets, the proposed CWO-CESN-based IDS model attained accuracies of 99%, 99.5%, and 99.8% for the detection of different attacks for NSL-KDD, WSN-DS and IoT-23 datasets, respectively, and ensured secured and reliable application in significant fields.

LocoMMotion: a study of real-life current standards of care in triple-class exposed patients with relapsed/refractory multiple myeloma - 2-year follow-up (final analysis).

Treatment of relapsed/refractory multiple myeloma (RRMM) is challenging as patients exhaust all available therapies and the disease becomes refractory to standard drug classes. Here we report the final results of LocoMMotion, the first prospective study of real-world clinical practice (RWCP) in triple-class exposed (TCE) patients with RRMM, with a median follow-up of 26.4 months (range, 0.1-35.0). Patients (N = 248) had received median 4 prior LOT (range, 2-13) at enrollment. 91 unique regimens were used in index LOT. Overall response rate was 31.9% (95% CI, 26.1-38.0), median progression-free survival (PFS) was 4.6 months (95% CI, 3.9-5.6) and median overall survival was 13.8 months (95% CI, 10.8-17.0). 152 patients (61.3%) had subsequent LOTs with 134 unique regimens, of which 78 were used in first subsequent LOT. Median PFS2 (from start of study through first subsequent LOT) was 10.8 months (95% CI, 8.4-13.0). 158 patients died on study, 67.7% due to progressive disease. Additional subgroup analyses and long-term safety summaries are reported. The high number of RWCP treatment regimens utilized and poor clinical outcomes confirm a lack of standardized treatment for TCE patients with RRMM, highlighting the need for new treatments with novel mechanisms.

Cluster Randomized Controlled Trial of a Standard Versus Postures-Only Yoga Session: Potential Self-Regulatory and Neurophysiological Mechanisms of an Ancient Practice.

It is well-recognized that maintaining a regular yogic practice is associated with numerous physical and psychological health benefits. However, few studies have explored the possible psychological and neurophysiological mediators through which the component parts of yoga-ethics, breath regulation, postures, and meditation-work to produce salutary effects. To address this gap, we conducted a cluster randomized trial to test the following set of theory-based mechanisms: emotion regulation for ethics, self-control for breath regulation, discomfort and distress tolerance for postures, and mindfulness for meditation. We also explored yoga's effects on the autonomic nervous system by examining salivary acetylcholinesterase levels. Participants (N = 260) were randomly assigned in clusters (n = 37) to a single, hour-long standard or postures-only yoga class. Findings suggest that a single yoga class may confer both psychological and neurophysiological benefits, yet there were few differences between the two types of yoga classes. Pre- to post-session main effects of time, all in the expected direction, emerged for five of eight theoretical mediators, as well as for salivary acetylcholinesterase levels. Time X condition interactions observed for three of the mediators-cognitive reappraisal, discomfort tolerance, and expressive suppression-along with findings from the exploratory mediation analysis suggest potential unique benefits of the two yoga sessions for certain outcomes. Additional studies are needed to replicate these results and to test other potential mediators and/or primary outcomes through which yoga might work to promote health.

A novel approach for the numerical solution of nonlinear Fredholm integral equations using Hosoya polynomial method

Abstract In this paper, we study the graph theoretical polynomial known as the Hosoya polynomial obtained from one of the standard classes of graphs called path. Using this polynomial applied for the numerical solution of the nonlinear Fredholm integral equation, which reduces in the algebraic system of equation with collocation points, then solving this system using Newton’s iterative with the help of MATLAB, we obtain the required approximate solution. The desired results in terms of a set of continuous polynomials over a closed interval [0, 1]. Illustrative applications show the efficiency, accuracy and validity of the proposed technique.

Comparative evaluation of plasma biomarkers of Schistosoma haematobium infection in endemic populations from Burkina Faso.

Infection with Schistosoma haematobium causes urogenital disease associated with organ disfunction, bleeding, pain, and higher susceptibility to infections and cancer. Timely and accurate diagnosis is crucial for prompt and appropriate treatment as well as surveillance efforts, and the use of plasma biomarkers offers important advantages over parasitological examination of urine, including increased sensitivity and the possibility to use the same specimen for multiple investigations. The present study aims to evaluate the diagnostic performance of different plasma biomarkers in endemic populations from Burkina Faso, West Africa. Schistosoma spp. Circulating Anodic Antigen (CAA), cell free S. haematobium DNA (cfDNA), class M and G antibodies against S. haematobium Soluble Worm Antigen Preparation (SWAP) and Soluble Egg Antigen (SEA) were measured in 406 plasma samples. Results of each biomarker test were compared to those of CAA, a Composite Reference Standard (CRS) and Latent Class Analysis (LCA). An identical proportion of positive samples (29%) was observed as a result of CAA and cfDNA testing, with a substantial agreement (84%, Cohen k = 0.62) between the results of the two tests, and a comparable agreement with the results of CRS and LCA. A higher positivity was observed, as expected, as a result of specific antibody testing (47%-72%), with IgG showing a higher agreement than IgM with the three references. Also, higher IgG levels were observed in current vs past infection, and ROC analysis identified optimal cutoff values for improved testing accuracy. This study provides compelling evidence that can inform the choice of the most appropriate diagnostic plasma biomarker for urogenital schistosomiasis in endemic areas, depending on the purpose, context, and available resources for testing. Either CAA or cfDNA testing can be used for the diagnosis of patients and for epidemiological investigations, even in absence of urine filtration microscopy, whereas anti-SWAP or anti-SEA IgG can be employed for surveillance and integrated monitoring of control interventions against poverty-associated diseases.

Standardizing Chemotherapy Education to Improve Patient Satisfaction at an Ambulatory Oncology Center.

This quality improvement project implemented a standardized chemotherapy class for newly diagnosed patients receiving oncology care at an urban ambulatory oncology center. Results showed high scores on patient satisfaction qu.

Matters of assuring functional dependability compliance of digital manufacturing

Aim. To propose a methodological approach to ensuring the functional dependability of industrial facilities using SMART documents in the context of import-independent digital processes. Methods. The evolving applicability of the dependability theory and information systems design has defined the methodological provisions for algorithmising the application of SMART standards for minimising functional faults and failures of industrial processes. Digital models of production lines were examined as tools for machine-recognisable representation of standards. The proposed form of regulatory documents is intended to enable the transition from machine-readable data to machine-understandable content. Findings. The application of the proposed methods of content systematisation in the context of manufacturing process standardisation shows that the classification of process data based on standardised characteristics of digital information exchange processes is one of the key methodological provisions ensuring unambiguous interpretation of regulatory requirements. Specialised projects often use individual provisions rather than complete regulatory documents. In this case, in order to ensure machine-understandability of the developed content, it is proposed using special identifiers, e.g., «paragraph», «graphic object», «table cell». The deployment of such identification tools will allow creating a class of SMART standards that collect into a single document the provisions regarding the delivery of intended results in situations when the operating equipment is diverse. Publications created using intelligent processing of SMART documents are considered as containers of structured and unstructured data that take into account the conditions of particular projects. The existing dynamics of the demand for advanced industrial products often determines the admissibility of its industrial production by various companies with the parallel use of unique technologies. The conditions of such an organisation impose their limitations on the comparability of various process requirements, therefore, it is proposed to harmonise the presentation of the results of managerial, design, and process engineering solutions using the term «manufacturing system asset». The introduction of relative result estimates as part of the digital format is an efficient mechanism for ensuring management consistency. In this context, the importance of a uniform regulatory framework as the foundation for such assessments is growing. As one of the ways of minimising faults and failures of industrial equipment, it is proposed to create a situation centre with a decision support system based on the regulatory data with harmonised manufacturing system asset requirements. An experience of Industry 4.0 regulation is described in the review of the ISA digital manufacturing series of standards. It was shown that the deployment of SMART standards improves the stability of interaction between information management systems and the functional dependability of production lines. The introduction of provisions regulating the use of algorithms for the development, editing, and examination of draft regulatory documents into the content machine-understandability facilities is an important factor in ensuring the functional dependability of products. Conclusions. The presented approach is focused on the methodology for developing standards that take into account requirements from various subject areas. In the context of ongoing import substitution in manufacturing, the paper examines the feasibility of minimizing digital process faults and failures caused by the use of regulatory frameworks that are not equivalent to the provisions of international documents and take into account the specificity of Russian industrial systems.

Effects of prenatal yoga exercise on hepcidin and ferritin levels in pregnant women

Prenatal yoga is a modification of hatha yoga tailored for pregnant women. This exercise is recommended throughout pregnancy. This study aimed to determine the effect of prenatal yoga exercise on the serum levels of hepcidin and ferritin in pregnant women. The study employed a quasi-experimental design with a pre- and post-test. A total of 134 second-trimester pregnant women participated and were divided into two groups (intervention group = 62, control group = 72). The intervention group received prenatal yoga exercises for 8 weeks (twice a week), while the control group attended only the standard pregnancy class. Each group was further divided into subgroups: anemic pregnant women, normal pregnant women, and overweight pregnant women. Demographic data were obtained through a basic questionnaire, while pre-pregnancy BMI data, obstetric history, and pregnancy examinations were sourced from the KIA book. Serum hepcidin levels significantly decreased in pregnant women who participated in prenatal yoga exercises (p=0.002), with the highest significance observed in overweight pregnant women (p=0.001). The average serum ferritin levels increased in all groups but were not statistically significant (p=0.759). Regular prenatal yoga exercises can reduce serum hepcidin levels in pregnant women, with a significant reduction noted in overweight pregnant women. Ferritin levels might be influenced by iron supplementation.

The impact of virtual reality haptic simulators in pre-clinical restorative dentistry: a qualitative enquiry into dental students’ perceptions

PurposeIn the realm of restorative dentistry, the integration of virtual reality haptic simulation (VRHS) for learning operative skills has garnered varied perceptions among dental students. Therefore, the aim of this study was to delve deep into undergraduates dental students’ perceptions related to the impact of VRHS in pre-clinical restorative dentistry.MethodsA homogeneous purposive sampling method was utilized to gather data from third-year undergraduate dental students (n = 23) at the College of Dental Medicine, Qatar University, to thoroughly investigate their views on the impact of VRHS on their learning experience in preparing a standard class I cavity. An explorative qualitative method using face-to-face focus group sessions were conducted in English during 2023. Focus group sessions were recorded and transcribed using Microsoft Teams. Two authors independently read the transcripts, coded the text, and manually analyzed text using an inductive thematic approach.ResultsA total of 21 (91.3%) students participated in this study. Analysis of 3 focus group interviews revealed five primary themes summarized with the term “MASTR” (M = manual dexterity, A = assessment, S = sequence, T = training, and R = realism). Based on frequency of reported themes, students perceived realism/ lifelike nature of VRHS requiring further enhancement to achieve the desired learning objective.ConclusionAlthough, VRHS play a crucial role in modern dental education, offering innovative solutions for training, evaluation, and feedback, the need to enhance their ability to simulate real-life dental procedures and learning environment (realism), coupled with interactive and immersive learning experiences were the most frequently raised theme by students. In terms of curriculum design and learning pedagogies, dental educators should consider the appropriate sequence when integrating VRHS within the undergraduate curricula.

A learning-based model predictive control scheme for injection speed tracking in injection molding process

Injection molding is a pivotal industrial process renowned for its high production speed, efficiency, and automation. Controlling the motion speed of injection molding machines is a crucial factor that influences production processes, directly affecting product quality and efficiency. This paper aims to tackle the challenge of achieving optimal tracking control of injection speed in a standard class of injection molding machines (IMMs) characterized by nonlinear dynamics. To achieve this goal, we propose a learning-based model predictive control (LMPC) scheme that incorporates Gaussian process regression (GPR) to predict and model uncertainty in the injection molding process (IMP). Specifically, the scheme formulates a nonlinear tracking control problem for injection speed, utilizing a GPR-based learning residual model to capture uncertainty and provide accurate predictions. It learns the dynamics model and historical data of the IMM, automatically adjusting the injection speed according to target requirements for optimal production control. Additionally, the optimization problem is efficiently solved using a control-constrained differential dynamic programming approach. Finally, we conduct comprehensive numerical experiments to demonstrate the effectiveness and efficiency of the proposed LMPC scheme for controlling injection speed in IMP.

Generous teacher: Good at distilling knowledge for student learning

Knowledge distillation is a technique that aims to transfer valuable knowledge from a large, well-trained model (the teacher) to a lightweight model (the student), with the primary goal of improving the student's performance on a given task. In recent years, mainstream distillation methods have focused on modifying student learning styles, resulting in less attention being paid to the knowledge provided by the teacher. However, upon re-examining the knowledge transferred by the teacher, we find that it still has untapped potential, which is crucial to bridging the performance gap between teachers and students. Therefore, we study knowledge distillation from the teacher's perspective and introduce a novel teacher knowledge enhancement method termed “Generous Teacher.” The Generous Teacher is a specially trained teacher model that can provide more valuable knowledge for the student model. This is achieved by integrating a standardly trained teacher (Standard Teacher) to assist in the training process of the Generous Teacher. As a result, the Generous Teacher accomplishes the task at hand and assimilates distilled knowledge from the Standard Teacher, effectively adapting to distillation teaching in advance. Specifically, we recognize that non-target class knowledge plays a crucial role in improving the distillation effect for students. To leverage this, we decouple logit outputs and selectively use the Standard Teacher's non-target class knowledge to enhance the Generous Teacher. By setting the temperature as a multiple of the logit standard deviation, we ensure that the additional knowledge absorbed by the Generous Teacher is more suitable for student distillation. Experimental results on standard benchmarks demonstrate that the Generous Teacher surpasses the Standard Teacher in terms of accuracy when applied to standard knowledge distillation. Furthermore, the Generous Teacher can be seamlessly integrated into existing distillation methods, bringing general improvements at a low additional computational cost. The code will be publicly available at https://github.com/EifelTing/Generous-Teacher.

Progressive Muscle Relaxation Training During Pregnancy: Effects on Mental State, Delivery and Labour Pain - a Prospective Study.

Progressive muscle relaxation is a widely used technique for relaxation, but studies are rare about efficacy on pregnancy and perinatal outcomes. Aim of our study was to determine whether progressive muscle relaxation affects anxiety and depression levels of pregnant women, pregnancy outcomes, labour pain and analgesic requirements. 156 pregnant women were enrolled in a prospective non-randomized controlled cohort study. The control group received standard antenatal care and classes only, while the intervention group additionally received progressive muscle relaxation training once a week for six weeks. Anxiety, depression and current strain were measured in a pretest-posttest experimental design using self-report scales at baseline (1 st survey), 5 weeks later (2 nd survey) and during puerperium (3 rd survey). Numeric Rating scales were used for measuring labour pain and satisfaction with analgesic treatment in the 3 rd survey. Fifty complete questionnaires from each group were analysed. There were no differences in mental status between the groups at baseline. Progressive muscle relaxation training significantly reduced depression levels. Trait anxiety, reflecting a person's basic anxiety decreased significantly in both groups during puerperium. No differences in pregnancy outcomes, labour pain and analgesic requirements could be shown within both groups in general. Nonetheless, women attending more than five progressive muscle relaxation courses reported significantly less labour pain within the group and compared to controls. Pregnant women could benefit from progressive muscle relaxation training if used continuously. Relaxation methods should be a substantial part of prenatal care and available for everyone.

TopoGeoFusion: Integrating object topology based feature computation methods into geometrical feature analysis to enhance classification performance

This study used smartphone captured RGB images of gooseberries to automatically sort into standard, premium, or rejected categories based on topology. Main challenges addressed include, separation of touching or overlapping fruits into individual entities and new method called 'TopoGeoFusion' that combines basic geometrical features with topology aware features computed from the fruits to assess the grade or maturity. Quality assessment helps in grading the fruit to determine market suitability and intelligent camera applications. Computer Vision-based techniques have been applied to automatically grade the quality of gooseberries as standard, premium, or rejected according to fruit maturity. Smartphone-captured images of 1697 Indian Star Gooseberries are contributed to the study. This work acquired images consisting multiple fruits with overlapping and non-overlapping boundaries for concurrent quality assessment. Multiple classifiers such as Random Forest, SVM, Naive Bayes, Decision Tree, and KNN were applied to grade the gooseberry fruit. Random Forest classification with a fusion feature model resulted in an accuracy of 100 % towards reject, standard, and premium classes for test sets with four training strategies. The proposed segmentation model proves reliable in fruit detection & extraction with an average mAP of 0.56, resulting in an acceptable model for grade assessment.•The study highlights the effectiveness of TopoGeoFusion in automating the grading process of gooseberry fruits using topologically computed features.•The developed models exhibit high accuracy and reliability, even in challenging scenarios such as overlapping and touching fruits.•The method provides the technique to detect and extract the occluded objects and compute the features based on the partial object's topology.

A Semester of Hatha Yoga Has Comparable Effects to Physical Education for Children in Terms of Physical Activity and Psychosocial Indicators.

As research on yoga with school children is growing, this study investigated the effects a semester of yoga versus physical education on children's physical activity and psychosocial indicators. Physical activity and psychosocial variables were assessed at Time 1 (January/February) and Time 2 (April/May) through self-report surveys (n = 157; 63% female, age: m = 10.38, sd = 0.81 years) for the intervention (Hatha yoga classes) and control groups (standard physical education classes). The intervention group was also observed regarding pedometer and System for Observing Fitness Instruction Time data. The results revealed a trend towards an increase for the control and a decrease for the intervention group in stress levels. The pedometer results showed a trend towards an increasing number of steps/min. Skill practice had the greatest increase with time dedicated to that activity. The observation results revealed a significant difference in activity from Time 1 to Time 2. The major differences were decreases in sitting and being very active, and an increase in walking. Given the observation data and our study design limitations, the study results showed similarities between Hatha yoga and physical education in terms of increasing physical activity levels and psychosocial variables; thus, yoga may be a viable alternative to children's physical education in this regard.