Balance Error Research Articles

Use of Reactive Balance Assessments With Clinical Baseline Concussion Assessments in Collegiate Athletes.

Current clinical concussion evaluations assess balance deficits using static or dynamic balance tasks while largely ignoring reactive balance. Including a reactive balance assessment might provide a more comprehensive concussion evaluation. To identify redundancy in current clinical baseline assessments of concussion and determine whether reactive balance adds unique information to these evaluations. Cross-sectional study. Clinical assessment. A total of 279 healthy National Collegiate Athletic Association Division I athletes. Two cohorts of data were collected at the beginning of the athletic season. For cohort 1 (n = 191), the Immediate Post-Concussion Assessment and Cognitive Tool, instrumented modified push and release (I-mP&R), and Balance Error Scoring System (BESS) were administered. For cohort 2 (n = 88), the I-mP&R, BESS, timed tandem gait, walking with eyes closed, and clinical reaction time were administered. The strengths of the relationships between the Immediate Post-Concussion Assessment and Cognitive Tool cognitive indices, mP&R clinical score, instrumented measures (BESS sway; I-mP&R time to stability, latency, and step length), BESS score, timed tandem gait, walking time to completion, and clinical reaction time were characterized. The strongest interinstrument correlation value was between single-task time to stability from the I-mP&R and clinical reaction time but was considered weak (r = 0.35, P = .001). The mP&R and I-mP&R clinical scores were weakly associated with the other assessments. Weak correlations between interassessment variables indicated that little redundancy was present in the current clinical evaluations. Furthermore, reactive balance represents a unique domain of function that may improve the comprehensiveness of clinical assessments.

Youth Preseason Performance on the Sport Concussion Assessment Tool 5 Across Multiple Sports.

To examine preseason Sport Concussion Assessment Tool 5 (SCAT5) performance of adolescent sport participants by environment (in-person/virtual), sex, age, concussion history, collision/noncollision sport participation, and self-reported medical diagnoses. Cross-sectional. Canadian community and high-school sport settings. Three thousand eight hundred five adolescent (2493 male, 1275 female, and 37 did not disclose; 11- to 19-year-old) sport participants. Sport Concussion Assessment Tool 5 administration method (in-person/virtual), sex (male/female/unreported), age (years), concussion history (0/1/2/3+), collision/noncollision sport participant, and self-reported medical diagnoses [attention deficit disorder or attention-deficit/hyperactivity disorder, headache/migraine, learning disability, and psychiatric disorder (ie, anxiety/depression/other)]. Preseason SCAT5 outcomes including total number of symptoms (TNS; /22), symptom severity score (SSS; /132), Standardized Assessment of Concussion (SAC; /50), and modified Balance Error Scoring System (mBESS; /30). Multiple multilevel linear or Poisson regression complete case analyses adjusting for clustering and robust standard errors, with β-coefficients (95% CI) back-transformed to indicate an increase/decrease in SCAT5 subdomains when relevant for clinical interpretation. Virtual (V) performance was associated with fewer symptoms reported [TNS Difference V-IP = -1.53 (95% CI, -2.22 to -0.85)], lower SSS [-2.49 (95% CI, -4.41 to -0.58)], and fewer mBESS errors (IP) [-0.52 (95% CI, -0.77 to -0.27)] compared with in-person. For every one-year increase in age, more symptoms [TNS = 0.22 (95% CI, 0.01-0.44)], higher SSS [0.52 (95% CI, 0.01-1.06)], higher SAC [0.27 (95% CI, 0.15-0.38), and poorer balance [mBESS = -0.19 (-0.28 to -0.09)] were observed. Differences between males and females were also seen across all SCAT5 outcomes. Individuals reporting any medical diagnosis or 3+ concussion history also reported more symptoms (TNS) and higher SSS than those who did not. Administration environment, sex, age, concussion history, and medical diagnoses were associated with SCAT5 subdomains and are important considerations when interpreting the SCAT5 results.

Sex Differences in Recovery Trajectories of Assessments for Sport-Related Concussion Among NCAA Athletes: A CARE Consortium Study.

The purpose of this study was to determine sex differences in recovery trajectories of assessments for sport-related concussion using Concussion Assessment, Research and Education (CARE) Consortium data. National Collegiate Athletic Association athletes (N = 906; 61% female) from sex-comparable sports completed a pre-season baseline assessment and post-sport-related concussion assessments within 6h of injury, 24-48h, when they initiated their return to play progression, when they were cleared for unrestricted return to play, and 6months post-injury. Assessments included the Standardized Assessment of Concussion, Balance Error Scoring System, Brief Symptom Inventory-18, Immediate Post-concussion Assessment and Cognitive Testing (ImPACT), Sport Concussion Assessment Tool-3 symptom evaluation, Clinical Reaction Time, King-Devick test, Vestibular Ocular Motor Screen, 12-item Short-Form Health Survey, Hospital Anxiety and Depression Scale, and Satisfaction with Life Scale. Only the Vestibular Ocular Motor Screen Total Symptom Score at the 24-48h timepoint (p = 0.005) was statistically significantly different between sexes. Specifically, female athletes (mean = 60.2, 95% confidence interval [CI] 51.5-70.4) had higher Vestibular Ocular Motor Screen Total Symptom Scores than male athletes (mean = 36.9, 95% CI 27.6-49.3), but this difference resolved by the time of return-to-play initiation (female athletes, mean = 1.8, 95% CI 1.1-2.9; male athletes, mean = 4.1, 95% CI 1.5-10.9). Sport-related concussion recovery trajectories for most assessments were similar for female and male National Collegiate Athletic Association athletes except for Vestibular Ocular Motor Screen symptoms within 48h of sport-related concussion, which was greater in female athletes. Female athletes had a greater symptom burden across all timepoints, suggesting that cross-sectional observations may indicate sex differences despite similar recovery trajectories.

PDFF-CNN: An attention-guided dynamic multi-orientation feature fusion method for gestational age prediction on imbalanced fetal brain MRI dataset.

Fetal brain magnetic resonance imaging (MRI)-based gestational age prediction has been widely used to characterize normal fetal brain development and diagnose congenital brainmalformations. The uncertainty of fetal position and external interference leads to variable localization and direction of the fetal brain. In addition, pregnant women typically concentrate on receiving MRI scans during the fetal anomaly scanning week, leading to an imbalanced distribution of fetal brain MRI data. The above-mentioned problems pose great challenges for deep learning-based fetal brain MRI gestational ageprediction. In this study, a pyramid squeeze attention (PSA)-guided dynamic feature fusion CNN (PDFF-CNN) is proposed to robustly predict gestational ages from fetal brain MRI images on an imbalanced dataset. PDFF-CNN contains four components: transformation module, feature extraction module, dynamic feature fusion module, and balanced mean square error (MSE) loss. The transformation and feature extraction modules are employed by using the PSA to learn multiscale and multi-orientation feature representations in a parallel weight-sharing Siamese network. The dynamic feature fusion module automatically learns the weights of feature vectors generated in the feature extraction module to dynamically fuse multiscale and multi-orientation brain sulci and gyri features. Considering the fact of the imbalanced dataset, the balanced MSE loss is used to mitigate the negative impact of imbalanced data distribution on gestational age predictionperformance. Evaluated on an imbalanced fetal brain MRI dataset of 1327 routine clinical T2-weighted MRI images from 157 subjects, PDFF-CNN achieved promising gestational age prediction performance with an overall mean absolute error of 0.848 weeks and an of 0.904. Furthermore, the attention activation maps of PDFF-CNN were derived, which revealed regional features that contributed to gestational age prediction at each gestationalstage. These results suggest that the proposed PDFF-CNN might have broad clinical applicability in guiding treatment interventions and delivery planning, which has the potential to be helpful with prenatal diagnosis.

Economy and carbon emissions optimization of different provinces or regions in China using an improved temporal attention mechanism based on gate recurrent unit

With the implementation of 14th Five-Year Plan in China and the completion of the poverty alleviation task, the economy in China has made great progress. However, the carbon dioxide (CO2) emission has also increased, which will lead to the greenhouse effect for threatening economic development and affecting people's lives. Global warming has become the greatest challenge to contemporary economic and social development, and the transition to low-carbon economy has become a general trend of world economic development. Meanwhile, the traditional neural network method for predicting the economy and CO2 emissions is not accurate and objective because of the unbalanced labeling of training data and the inability to capture the relationship between time series data. Therefore, an economy and CO2 emission prediction model based on temporal attention Gate Recurrent Unit (GRU) (TA-GRU) with loss function Balanced Mean Square Error (BMSE) (BTA-GRU) mechanism is proposed to analyze and optimize the energy structure of 27 provincial-level administrative regions in China. Due to the unbalanced data labels in the different energy structure of different regions, the improved loss function BMSE is used to solve the problem of unbalanced labels from the perspective of statistics. Then, with coal, gasoline, petroleum, coke, fuel oil, diesel, natural gas and crude oil as inputs, the per capita Gross Domestic Product (GDP) as the desired output and the CO2 as the undesired output, the GRU can better capture time step dependencies in time series, and the attention mechanism can assign different weights to each time step, so that the proposed method can better learn data features and improve the accuracy of the an economic and CO2 emission prediction model. Finally, the CO2 emission and economic prediction model of different provincial-level administrative regions in China is established based on this proposed method. The experimental results show that compared with other mainstream time series data prediction models, this proposed model has better stability and practicability, and its mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) reach 3.43, 3.28% and 2449.57, respectively. Furthermore, according to the actual situation of efficient provincial administrative regions, the energy structure of inefficient provincial administrative regions can be adjusted and optimized. Such as the energy structure of Hebei Province can be adjusted according to the energy structure of Beijing, it is estimated that the CO2 emissions of Hebei will be reduced by 82,972.43 ×10000 tons (WT) and its per capita GDP will be increased by 115,856 Yuan. Moreover, Chinese provincial governments should vigorously promote the use of clean energy, accelerate the establishment of a sound green low-carbon circular development economic system, and help achieve the goal of carbon neutrality.

A Cenozoic Record of Deep Oceanic Zn Isotopic Composition in Ferromanganese Crusts

The zinc (Zn) isotopic composition (δ66Zn) of the deep ocean (>1000 m) can provide insights into the carbon cycle, the biological pump, and hydrothermal activity. However, we have an incomplete view of the temporal and spatial evolution of deep-ocean Zn isotopes. Here, we present new δ66Zn values of Fe-Mn crusts from the Pacific Ocean, which we used to reconstruct the evolution of deep-ocean δ66Zn for the Cenozoic. Our results suggest that the δ66Zn values remain stable in the deep Pacific Ocean at around ~ 0.5‰ through the Cenozoic. Our results limit the extent of change in organic zinc burial through the Cenozoic. However, given uncertainties in the global mass balance and analytical error, variations of roughly 20% in organic zinc burial are still possible.

Effects of blood flow restriction on spine postural control using a robotic platform: A pilot randomized cross-over study.

Blood flow restriction (BFR) training improves muscle strength and functional outcomes, but the proprioceptive implications of this technique in the rehabilitation field are still unknown. The present study aimed at assessing the effects of BFR in terms of stabilometric and balance performance. In this pilot randomized cross-over study, healthy young adults were included and randomly assigned to Groups A and B. Both groups underwent a postural assessment with and without wearing a BFR device. Study participants of Group A underwent postural baseline assessment wearing BFR and then removed BFR for further evaluations, whereas subjects in Group B performed the baseline assessment without BFR and then with BFR. Stabilometric and balance performance were assessed by the robotic platform Hunova, the Balance Error Scoring System (BESS), the self-reported perceived balance (7-point Likert scale), and discomfort self-rated assessment. Moreover, the safety profile was recorded. Fourteen subjects were included and randomly assigned to Group A (n: 7) and Group B (n: 7). Significant differences were shown in balance tests in static conditions performed on the Hunova robot platform in terms of average distance RMS (root-mean-square) with open eyes (OE), anteroposterior (AP) trunk oscillation range with OE, mediolateral (ML) average speed of oscillation with OE, and total excursion AP range with closed eyes (CE) (BFR: 3.44 ± 1.06; without BFR: 2.75 ± 0.72; p= 0.041). Moreover, elastic balance test showed differences in Romberg index (BFR: 0.16 ±0.16; without BFR: 0.09 ± 0.07; p= 0.047). No adverse events were reported. Taken together, our data showed that BFR affects balance performance of healthy subjects. Further studies are needed to better characterize the possible role of BFR treatment in the context of a specific rehabilitation protocol.

Evaluation of Multiple Classifier Systems for Mapping Different Hierarchical Levels of Forest Ecosystems in the Mediterranean Region Using Sentinel-2, Sentinel-1, and ICESat-2 Data

The conservation and management of forest areas require knowledge about their extent and attributes on multiple scales. The combination of multiple classifiers has been proposed as an attractive classification approach for improved accuracy and robustness that can efficiently exploit the complementary nature of diverse remote sensing data and the merits of individual classifiers. The aim of this study was to develop and evaluate multiple classifier systems (MCSs) within a cloud-based computing environment for multi-scale forest mapping in Northeastern Greece using passive and active remote sensing data. Five individual machine learning base classifiers were used for class discrimination across the three different hierarchy levels, and five ensemble approaches were used for combining them. In the case of the binary classification scheme in the upper level of the hierarchy for separating woody vegetation (forest and shrubs) from other land, the overall accuracy (OA) slightly increased with the use of the MCS approach, reaching 94%. At the lower hierarchical levels, when using the support vector machine (SVM) base classifier, OA reached 84.13% and 74.89% for forest type and species mapping, respectively, slightly outperforming the MCS approach. Yet, two MCS approaches demonstrated robust performance in terms of per-class accuracy, presenting the highest average F1 score across all classification experiments, indicating balanced misclassification errors across all classes. Since the competence of individual classifiers is dependent on individual scene settings and data characteristics, we suggest that the adoption of MCS systems in efficient computing environments (i.e., cloud) could alleviate the need for algorithm benchmarking for Earth’s surface cover mapping.

Application and effects of physics-based and non-physics-based regularizations in artificial intelligence-based surrogate modelling for highly compressible subsurface flow

Artificial intelligence (AI)-based surrogate reservoir models (SRMs) can provide computationally feasible and accurate approximations to numerical simulations. An AI-based SRM is trained to a set of parameters that significantly reduces its variance. This can be done by either supervised or semi-supervised learning. The latter involves regularization of the model’s parameters using non-physics-based, physics-based or a combination of both regularization terms.Effective enforcement of the physics-based and non-physics-based regularizations can significantly reduce the variance of AI-based SRMs. Little study has been reported on the application and effects of regularization terms. Also, for highly compressible subsurface flow where strong nonlinearities exist, well-constructed composite AI-based architectures and regularizations are necessary for learning.This paper applies and studies the effects of various regularization terms for highly compressible subsurface flow; it proposes unique and effective techniques in AI-based surrogate development and training. The learning utilizes the discretized domain and boundary physics with derivatives obtained from both finite difference methods (FDM) and algorithmic differentiation (AD). The regularizations are partly enforced as a hard constraint in the network architecture using a trainable layer and as soft constraints using a multi-cost function. The soft constraints exploit a tank material balance and time-discretization numerical errors, in addition to the domain, boundary and non-physics-based L2 regularization terms. The timely-trained AI-based surrogate predictions agree with those obtained from a numerical simulator.The regularization terms separately contribute to improved learning. The non-physics-based L2 norm if used in the right order of magnitude, improves grid block predictions. The tank material balance regularization term constrains the AI-based surrogate parameters to net domain accumulation, ensuring its reliability. The trainable hard enforcement layer enforces the initial condition and improves the predictions compared to other hard enforcement techniques. The discretized domain equation and time-discretization numerical errors allow for learning of variable timesteps, which give the best rounding-truncation error trade-off and improve the predictions compared to those of fixed timesteps. The AI-based surrogate, effectively trained by semi-supervision, can be reliably used as a state-dependent model in domain analysis like sensitivity and data assimilation.

Algorithmic fairness in precision psychiatry: analysis of prediction models in individuals at clinical high risk for psychosis.

Computational models offer promising potential for personalised treatment of psychiatric diseases. For their clinical deployment, fairness must be evaluated alongside accuracy. Fairness requires predictive models to not unfairly disadvantage specific demographic groups. Failure to assess model fairness prior to use risks perpetuating healthcare inequalities. Despite its importance, empirical investigation of fairness in predictive models for psychiatry remains scarce. To evaluate fairness in prediction models for development of psychosis and functional outcome. Using data from the PRONIA study, we examined fairness in 13 published models for prediction of transition to psychosis (n = 11) and functional outcome (n = 2) in people at clinical high risk for psychosis or with recent-onset depression. Using accuracy equality, predictive parity, false-positive error rate balance and false-negative error rate balance, we evaluated relevant fairness aspects for the demographic attributes 'gender' and 'educational attainment' and compared them with the fairness of clinicians' judgements. Our findings indicate systematic bias towards assigning less favourable outcomes to individuals with lower educational attainment in both prediction models and clinicians' judgements, resulting in higher false-positive rates in 7 of 11 models for transition to psychosis. Interestingly, the bias patterns observed in algorithmic predictions were not significantly more pronounced than those in clinicians' predictions. Educational bias was present in algorithmic and clinicians' predictions, assuming more favourable outcomes for individuals with higher educational level (years of education). This bias might lead to increased stigma and psychosocial burden in patients with lower educational attainment and suboptimal psychosis prevention in those with higher educational attainment.

Abstract 16786: Integrative Omics in Failing Myocardium Highlights Metabolic Substrate Utilization Defects in Human Heart Failure

Introduction: Changes in substrate utilization may be a biomarker of, or a contributor to, heart failure with reduced ejection fraction (HFrEF). Previous work has described circulating metabolites, proteins and transcripts that reflect myocardial bioenergetics, but direct assessment in relevant tissues is lacking. Here, we leverage integrative omics to identify tissue-level biomarkers and pathways of HFrEF that may play a role in pathogenesis. Methods: Myocardial tissue was obtained from 27 HFrEF donors and 21 non-failing hearts (noHF) from Duke University and the University of Colorado Medical Centers. Targeted (n=139 metabolites) and nontargeted (Metabolon, n=817) metabolic profiling was performed; in addition, 496 proteins were assayed (Olink) and ~31,000 transcripts were quantified via RNA sequencing. Individual metabolites were tested for association with HFrEF in models adjusted for age, sex and diabetes. Metabolomics, proteomics and RNA-seq data were integrated using block sparse partial least squares discriminant analysis (sPLS-DA) to identify correlated biomarkers that discriminate HFrEF from noHF hearts. Results: The targeted metabolomics platform primarily assayed metabolic fuel substrate pathways; 71 analytes (51%) were associated with HFrEF after controlling the false discovery rate (FDR) at 5%. Medium- and long-chain acylcarnitines (C10s-C18s) were significantly lower in tissue from failing hearts, while branched-chain amino acids (BCAA) and ketoacids were higher. In the nontargeted set, 293 metabolites (36%) differed between HFrEF and noHF. BCAA and acylcarnitine associations were recapitulated along with a decrease in malonylcarnitine. The sPLS-DA model selected 25 targeted and 50 nontargeted metabolites, 35 proteins and 35 transcripts, and successfully discriminated HFrEF from no HF (balanced error rate = 3.2%). 39/186 tested KEGG pathways were overrepresented in these analytes, including fatty acid metabolism (FDR q=0.002). Conclusions: Analysis of tissue-level metabolites suggests both decreased use of fatty acids as a fuel and disrupted enzymatic breakdown of BCAAs in the failing heart, while integrative omics pathway analysis also supports the association of disrupted fatty acid metabolism with HF.

Bayesian and E-Bayesian estimation based on constant-stress partially accelerated life testing for inverted Topp–Leone distribution

Abstract Accelerated or partially accelerated life tests are particularly significant in life testing experiments since they save time and cost. Partially accelerated life tests are carried out when the data from accelerated life testing cannot be extrapolated to usual conditions. The constant-stress partially accelerated life test is proposed in this study based on a Type-II censoring scheme and supposing that the lifetimes of units at usual conditions follow the inverted Topp–Leone distribution. The Bayes and E-Bayes estimators of the distribution parameter and the acceleration factor are derived. The balanced squared error loss function, which is a symmetric loss function, and the balanced linear exponential loss function, which is an asymmetric loss function, are considered for obtaining the Bayes and E-Bayes estimators. Based on informative gamma priors and uniform hyper-prior distributions, the estimators are obtained. Finally, the performance of the proposed Bayes and E-Bayes estimates is evaluated through a simulation study and an application using real datasets.

Maximizing the Accuracy of Adolescent Concussion Diagnosis Using Individual Elements of Common Standardized Clinical Assessment Tools.

Multiple clinical evaluation tools exist for adolescent concussion with various degrees of correlation, presenting challenges for clinicians in identifying which elements of these tools provide the greatest diagnostic utility. To determine the combination of elements from 4 commonly used clinical concussion batteries that maximize discrimination of adolescents with concussion from those without concussion. Cross-sectional study. Suburban school and concussion program of a tertiary care academic center. A total of 231 participants with concussion (from a suburban school and a concussion program) and 166 participants without concussion (from a suburban school) between the ages of 13 and 19 years. Individual elements of the visio-vestibular examination (VVE), Sport Concussion Assessment Tool, fifth edition (SCAT5; including the modified Balance Error Scoring System), King-Devick test (K-D), and Postconcussion Symptom Inventory (PCSI) were evaluated. The 24 subcomponents of these tests were grouped into interpretable factors using sparse principal component analysis. The 13 resultant factors were combined with demographic and clinical covariates into a logistic regression model and ranked by frequency of inclusion into the ideal model, and the predictive performance of the ideal model was compared with each of the clinical batteries using the area under the receiver operating characteristic curve (AUC). A cluster of 4 factors (factor 1 [VVE saccades and vestibulo-ocular reflex], factor 2 [modified Balance Error Scoring System double-legged stance], factor 3 [SCAT5/PCSI symptom scores], and factor 4 [K-D completion time]) emerged. A model fit with the top factors performed as well as each battery in predicting concussion status (AUC = 0.816 [95% CI = 0.731, 0.889]) compared with the SCAT5 (AUC = 0.784 [95% CI = 0.692, 0.866]), PCSI (AUC = 0.776 [95% CI = 0.674, 0.863]), VVE (AUC = 0.711 [95% CI = 0.602, 0.814]), and K-D (AUC = 0.708 [95% CI = 0.590, 0.819]). A multifaceted assessment for adolescents with concussion, comprising symptoms, attention, balance, and the visio-vestibular system, is critical. Current diagnostic batteries likely measure overlapping domains, and the sparse principal component analysis demonstrated strategies for streamlining comprehensive concussion assessment across a variety of settings.

65 Examining Neuropsychological Outcomes Among Youth Concussion Patients With and Without Neurodevelopmental Learning Disorders

Objective:The increasing incidence rates of concussive injuries, specifically among the youth age demographic, has led to the Center for Disease Control and Prevention to classify these injuries as a “silent epidemic.” While symptoms from concussion typically remit within four weeks, many experience symptoms that persist beyond what is typically expected. For children, the persistence of symptoms can have damaging side-effects that impact daily functioning and the progression through developmental and educational milestones. Recent research highlights factors that modify the nature of outcomes after a concussive injury. For youth-aged individuals, one such factor is the presence of premorbid conditions. A growing body of research suggests children with learning disorders (LDs) may be more susceptible to negative symptom outcomes across neuropsychological domains. Therefore, the purpose of this study is to further examine the influence of developmental learning disorders on concussion outcomes among youths.Participants and Methods:Youths between 7 and 19 years of age that sought care at a concussion specialty clinic were screened for inclusion in the current study. The final sample of included 54 patients who reported having a neurodevelopmental LD between the ages of 8 and 17 and 54 patients without LDs matched on age, race/ethnicity, and gender to serve as paired case-controls. Measures of post-concussive physical symptoms (Modified Balance Error Scoring System; Quality of Life in Neurological Disorders: Neuro-QoL-SD, Neuro-QoL-F), emotional state (BAI, BDI), parent report measure of behavioral manifestations of cognitive functioning (Behavioral Rating Inventory of Executive Function), and select measures from the CogState automated test battery (i.e., One-Back, Two-Back, Groton Maze Learning, and Groton Maze Recall) were administered during the patient’s first examination at the clinic. Patients were instructed by the provider to follow-up at the clinic between two-three weeks for a repeat examination. The current study examined concussion outcome variables via two (group: LD, control) by two (time: initial examination, follow-up examination) repeated measures ANCOVAs where time between injury and the first examination was included as a covariate to control for the duration of elapsed days since injury.Results:Regarding cognitive symptoms, parent reported behaviors associated with executive functioning symptoms increased over time to reach clinically significant levels for the LD group, while symptoms decreased and remained within normal limits for the control group. Performance-based measures of cognition revealed no significant interactions or group/time differences. Additionally, the LD group showed more intense balance problems compared to the control group and symptom trends suggest LD participants may be more susceptible to prolonged, clinically significant, balance problems. With respect to sleep disturbances and fatigue, symptoms remained within normal limits across groups and time. Depression and anxiety symptoms remained within normal limits across groups and time as well.Conclusions:Results highlight more intense balance problems and parent-reported executive dysfunction following concussion for youths with LDs compared to those without LDs. Additionally, though not significantly differentthan the control group, results reveal several symptom domains in which the level of dysfunction meets clinical significance for the LD group participants only. As such, this study highlights the continued need for the scientific study of risk factors for vulnerable populations to aid in assessment and prevention efforts, especially for youths with LD.

Correlation Of Medial Longitudinal Arch Height With Postural Stability, Sensory Integration, Balance And Fall Risk Among Healthy Young Adults.

A cross-sectional analytical study was conducted on 165 healthy young adults. With pes rectus and pes planus to correlate the medial longitudinal arch height with postural stability, sensory integration of balance and fall risk. Persons with pes cavus, congenital foot anomalies other than pes planus, leg length discrepancies, recent history of trauma, lower limb amputations, history of serious foot injury, ligamentous laxity, or an active inflammatory disorder were excluded. Outcome measurements included normalised truncated navicular height (NTNH), Chippaux Smirak index (CSI), athletic single leg stability (ASLS) index, fall risk (FR) index, postural stability (PS), clinical test of sensory integration of balance (CTSIB), and balance error scoring system (BESS). Spearman correlation and Mann Whitney U test were used for data analysis. CSI and NTNH were noted to have no significant correlation (p<0.05) with PS, FR, CTSIB, ASLS and BESS among healthy young adults. Males were observed to have poorer balance and fall risk as compared to females.

A novel sampling approach for prediction of post-earthquake damage to buildings

Predicting damage to buildings using field-based samples is important for crisis management after an earthquake. Because building damage in seismic regions is spatially correlated, neighboring samples will contain similar information. However, if they are treated as independent observations, the total sample set will not provide sufficient information. To prevent this, sample locations must be dispersed throughout the region and the spatial correlation of observations must be taken into account. The current study proposes a two-stage sampling approach to select well-dispersed samples of small size containing substantial information. The sample locations have been chosen using the Halton iterative partitioning method and individual buildings were randomly selected for each location. The selected sample buildings were employed in a kriging regression model to predict the damage ratio. Multiple factors were used in the model as predictor variables to increase the prediction accuracy. The proposed sampling approach was compared to other sampling methods in terms of spatial balance and prediction error using both simulated and real datasets from the 2017 Sarpol-e Zahab earthquake in Iran. The proposed approach provided better results than the other approaches. Although some factors affected the building damage in the actual data, a combination of fault strike angles and seismic intensity measures provided more accurate predictions than other combinations.

Optimizing baseline and post-concussion assessments through identification, confirmation, and equivalence of latent factor structures: Findings from the NCAA-DoD CARE Consortium

Objective: Concussion evaluations use a multidimensional assessment to evaluate unique patient function dimensions (e.g., subjective symptoms differ from balance assessments), but the overarching latent factor structure has not been empirically substantiated. Our objective was to determine the cumulative latent factor structure of pre-injury baseline and acute (<48-h) post-concussion assessment battery outcomes, and determine measurement equivalence among common factors in collegiate student-athletes. Methods: Collegiate student-athletes at baseline (n = 21,865) and post-concussion (n = 1,537) across 25-institutions completed standardized assessments. Individual items were used from the baseline and post-concussion assessments and consisted of: Sport Concussion Assessment Tool, Brief Symptom Inventory-18, Standardized Assessment of Concussion, Balance Error Scoring System, Immediate Post-Concussion Assessment and Cognitive Test, and vestibular-ocular motor screening. Exploratory factor analysis was used on half the baseline data, and confirmatory factor analysis on the remaining baseline data and post-concussion data separately. Measurement equivalence was assessed between sex, sport contact classification, concussion history, and time. Results: A 10-factor exploratory model was established and comprised of: depression, somatic, vestibulo-ocular, headache, postural stability, neurocognition, emotional, fatigue, cognitive, consciousness clouding. The 10-factor model was confirmed at baseline and post-concussion with strong measurement equivalence between timepoints. Strong to strict measurement equivalence was observed for sex, sport contact classification, and concussion history at both timepoints separately. Conclusion: Our findings established a robust 10-factor latent factor model equivalent across timepoints and common factors among healthy and concussed collegiate athletes. Clinicians can use these findings to target specific factors while reducing redundant elements to provide efficient, comprehensive post-concussion assessments.

The Effects of Core Stabilization Exercises on Respiratory Muscle Strength, Respiratory Functions, and Postural Control in Children With Hearing Loss: A Randomized Controlled Trial.

To evaluate the effects of core stabilization training on respiratory muscle strength, respiratory functions, and postural control in children with hearing loss. We conducted a randomized controlled trial at Bezmialem Vakif University, Faculty of Health Sciences, Division of Physiotherapy and Rehabilitation. Thirty children with hearing loss who were diagnosed with prelingual sensorineural hearing loss were randomly allocated to the study (n = 15, 12.20 ± 1.69 years) and control (n = 15, 11.87 ± 2.20 years) groups. Core stabilization training protocol was performed by the study group for 8 weeks, 5 days/week, whereas the control group received no training. The primary outcome measure was respiratory muscle strength. The secondary outcome measures were: spirometry, postural control tests on Biodex Balance System, and Balance Error Scoring System (BESS). Exercise adherence was recorded via a diary for home sessions. Outcomes were assessed at baseline and the end of the 8 weeks. In the study group, a significant improvement was observed in force vital capacity, peak expiratory flow of the spirometry test, maximum inspiratory pressure/expiratory pressure (MEP) of the respiratory muscle strength test; postural stability test except "anterior/posterior stability index", limits of stability test except "backward" and "forward/right", all parameters of the Modified Clinical Test for Sensory Interaction and Balance (mCTSIB) test except "eyes closed firm surface" of the Biodex Balance System, and total score of BESS. Group × time interaction was found in MEP, "overall" stability index in postural stability test, "overall," "left," and "backward left" of limits of stability test, all parameters of the mCTSIB, and BESS total. Core stabilization exercises were effective in improving respiratory muscle strength, spirometry, and postural control in children with hearing loss.

Effects of task-oriented balance training with sensory integration in post stroke patients

Background: Balance and functional mobility is greatly affected in stroke patients. Balance training integrated with sensory input is more beneficial for improving balance and mobility in stroke patients. Objective: To determine the effects of task-oriented balance training with and without sensory integration on balance, postural stability, and mobility in post-stroke patients. Methods: A randomized control trial was conducted at RHS Rehabilitation Centre, Islamabad. A total of n=60 post-stroke patients were included in the study through a non-probability purposive sampling technique. Male and female stroke patients above 40 years of age, having the ability to maintain a standing position without aid for at least 5 minutes, and patients on Grades II, III, IV on the Functional Mobility Scale were included in this study. They were randomly allocated into Group A (n=30) and Group B (n=30). Both groups received Task oriented balance training while group B was treated with additional sensory integration. The Berg Balance Scale, Dynamic Gait Index, Activities Specific Balance Confidence Scale, and Balance Error Scoring system were used for balance assessment. The assessment was done at baseline and after 6th week. Results: The mean age of the participants was 54.47 years. After 6 weeks of intervention, a significant difference was found in group A as compared to group B for Dynamic mobility measured by Dynamic gait index with p-value (p=0.06) and for balance measured by BBS with p-value (p=0.05). Conclusion: The task-oriented balance training with sensory integration is effective in improving dynamic balance and mobility in stroke patients. Keywords: balance training; Sensory Integration; stroke rehabilitation; task-oriented balance exercises.

Effect of aquatic therapy on motor skill and executive function in children with Autism Spectrum Disorder

Research has demonstrated the positive effects of different types of physical activity, especially aquatic therapy for children with autism spectrum disorder (ASD). As such, this study aimed to examine the effect of a response-oriented approach using aquatic exercise for children with ASD on motor skills and executive function. In this quasi-experimental design, 40 children aged 7–10 years were randomly assigned to one of two groups: a response-oriented aquatic exercise (ROA) group (n=20) or a control (CON) group (n=20). The Bruininks-Oseretsky Test of Motor Proficiency second edition – Short Form (BOTMP-2) and the Wisconsin Card Sorting Test (WCST) were used to assess motor skill and executive function, respectively. Significant (p≤0.05) differences were found between the ROA and CON groups in receiving (F=38.94, p=0.001) and throwing (F=33.05, p=0.001), static balance (F=44.89, p=0.002), dynamic balance (F=48.51, p=0.010), correct responses (F=3.60, p=0.010), conceptual responses (F=0.34, p=0.010) and perseverative errors (F=1.57, p=0.040). Aquatic exercise was found to be an effective intervention for children with ASD in that it decreases motor deficits and improves executive function. This study provides families, teachers and other specialists with exercise optionality that may be exciting and motivational to children with ASD due to its response-oriented approach.