Movement Patterns Research Articles

Dysbiosis of gut microbiota in COVID-19 is associated with intestinal DNA phage dynamics of lysogenic and lytic infection.

This study compared intestinal DNA phage dynamics and gut microbiota changes observed at the onset of coronavirus disease 2019 (COVID-19). The study participants included 19 healthy individuals and 19 patients with severe acute respiratory syndrome coronavirus 2 infection. Significant differences were observed in the diversity of the intestinal DNA virome after the onset of COVID-19 compared with that in healthy individuals. Classification by their tail morphology resulted in the order Caudovirales, a double-stranded DNA phage, accounting for >95% of all participants. In classifying phages based on host bacteria, a decreased number of phages infecting mainly the Clostridia class was observed immediately after the onset of COVID-19 and recovered over time. After the onset of COVID-19, two distinct movement patterns of intestinal phages and their host bacteria were observed: phage- and bacteria-predominant. The abundance of obligate anaerobes, such as Clostridium_sense_strict_1, Fusicatenibacter, and Romboutsia, and the phages hosting these bacteria decreased immediately after the onset of COVID-19, and faster phage recovery was observed compared with bacterial recovery. In contrast, the genus Staphylococcus, a facultative anaerobic bacterium, increased immediately after the onset of COVID-19, whereas the phages infecting Staphylococcus decreased. Furthermore, immediately after the onset of COVID-19, the percentage of lytic phages increased, whereas that of temperate phages decreased. These observations suggest that the gut microbiota dysbiosis observed immediately after the onset of COVID-19 may be linked to phage dynamics that control gut microbiota and may also affect the recovery from dysbiosis.IMPORTANCEBacteriophages infect and replicate with bacteria and archaea and are closely associated with intestinal bacteria. The symbiotic relationship between gut microbiota and bacteriophages is of interest, but it is challenging to study their dynamics in the human body over time. SARS-CoV-2 infection has been reported to alter the gut microbiota, which is involved in gut immune regulation and pathophysiology, although changes in the intestinal phages of patients with SARS-CoV-2 and their dynamic relationship with the gut microbiota remain unclear. SARS-CoV-2 infection, which follows a transient pathological course from disease onset to cure, may provide a reliable model to investigate these interactions in the gut environment. Therefore, this study aimed to elucidate the correlation between gut microbiota and intestinal DNA virome dynamics in COVID-19 pathogenesis. This study found that the dysbiosis observed in SARS-CoV-2 infection involves a growth strategy that depends on the phage or bacterial dominance.

A Study on the Impact of Different Delay Times on Rock Mass Throwing and Movement Characteristics Based on the FEM–SPH Method

Burst morphology is a crucial indicator for evaluating the effectiveness of blasting, as it directly reflects the actual state of the blasting results. The results of rock displacement following blasting partially reflect the effectiveness of throw blasting, while the rock ejection process serves as the macroscopic manifestation of the blasting method. To accurately assess the impact of different delay times on burst formation, this study addressed the issues of rock movement and ejection in underground blasting. Using three-dimensional modeling, we constructed a FEM–SPH model and utilized LS-DYNA numerical simulation software to investigate the movement patterns of rock in precise delayed blasting scenarios underground. This study explored the spatiotemporal evolution characteristics of rock movement post-blasting. Digital electronic detonators were used to set precise inter-row delay times of 25 ms, 50 ms, and 75 ms. The results revealed that the ejection distance of blasted rock in underground mining increased with longer inter-row delay times, while the slope angle of the blasted muck pile decreased as the delay time increased. Furthermore, at a micro level, the study found that a 75 ms delay created new free surfaces, providing effective compensation space for subsequent blasts, thereby improving blasting outcomes. Analysis of the 25 ms and 50 ms delay periods indicated a clamping effect on rock movement. Field comparisons of blasting results were conducted to validate the influence of precise delay times on the movement patterns and spatiotemporal evolution characteristics of blasted rock.

Roads are partial barriers to foraging solitary bees in an urban landscape.

Understanding how animals navigate novel heterogeneous landscapes is key to predicting species responses to land-use change. Roads are pervasive features of human-altered landscapes, known to alter movement patterns and habitat connectivity of vertebrates like small mammals and amphibians. However, less is known about how roads influence movement of insects, a knowledge gap that is especially glaring in light of recent investments in habitat plantings for insect pollinators along roads verges and medians. In this study, we experimentally investigate behavioral avoidance of roads by a solitary bee and explore whether landscape factors are associated with bee movement in urban Massachusetts, USA. Using mark-recapture surveys, we tracked individual solitary bee (Agapostemon virescens) foraging movements among floral patches separated by roads or grass lawn. We found that roads acted as partial barriers to movements of foraging bees, with road crossings nearly half as likely as along-road movements (36% vs. 64%). Movement probabilities were negatively associated with distance and the proportion of roadway between patches, and positively associated with higher floral resource density at the destination patch. Importantly, our findings also suggest that while roads impede bee movement, they are not complete barriers to dispersal of bees and/or transfer of pollen in urban landscapes. In the context of green space design, our findings suggest that prioritizing contiguous habitat and ensuring higher floral densities along road edges may enhance resource access for pollinators and mitigate the risk of ecological traps.

Neuro-Fuzzy Logic for Automatic Animation Scene Generation in Movie Arts in Digital Media Technology

Animation scene generation (ASG) is the best digital media tool for lifelike scenes, particularly for movies. Traditional animation methods are laborious, computationally intensive, and scalable. Thus, this work addresses animation production issues using NFL-ASG. Combining fuzzy logic with a convolution neural network may create more realistic animated situations with less human interaction and better learning. Convolutional model training uses animation scenarios’ complicated motion patterns, character interactions, and ambient factors. Deep learning and fuzzy logic might change animation by boosting production techniques and releasing digital media technological creativity. After testing the system on the Moana Island scene dataset, it achieved a perception analysis success rate of 0.981% and a minimal processing complexity of (n logn).

Executive function among adults with autism spectrum disorder: An eye-tracking study.

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social communication deficits and repetitive behaviors. Eye movement abnormalities are common in ASD, suggesting underlying cognitive impairments such as working memory and response inhibition. However, several investigations suggest deficits in inhibitory control and working memory among individuals with ASD, whereas others indicate performance levels similar to typically developing individuals. The variability observed in executive function (EF) profiles across various tasks and age cohorts underscores the intricate interplay between ASD and cognitive functioning. Therefore, to bridge these gaps and gain a comprehensive understanding of the specific components of EF affected by ASD, this study used eye-tracking technology to analyze cognitive processing. In the working memory task, the clinical group comprised adults with ASD (N = 52), aged between 25 and 47 years (M = 31.65 years, SD = 6.05), while the control group (non-ASD) included individuals (N = 53), aged between 25 and 55 years (M = 33.15 years, SD = 5.92). For the response inhibition task, the clinical group consisted of adults with ASD (N = 50), aged between 25 and 47 years (M = 31.66 years, SD = 6.153), and the control group comprising individuals (N = 56), aged between 25 and 55 years (M = 33.03 years, SD = 5.80). The study revealed that individuals with ASD demonstrate notable impairments and increased errors in EF, encompassing inhibitory control and working memory when compared to non-ASD individuals. Additionally, individuals with ASD exhibited differences in fixation duration, saccade count, and fixation count compared to their non-ASD counterparts, further highlighting the distinct cognitive profile associated with ASD. Analyzing EF in adults with ASD through eye movement patterns provides valuable insights into the underlying cognitive processes and attentional mechanisms at play.

Dynamic Bidirectional Associations Between Global Positioning System Mobility and Ecological Momentary Assessment of Mood Symptoms in Mood Disorders: Prospective Cohort Study.

Although significant research has explored the digital phenotype in mood disorders, the time-lagged and bidirectional relationship between mood and global positioning system (GPS) mobility remains relatively unexplored. Leveraging the widespread use of smartphones, we examined correlations between mood and behavioral changes, which could inform future scalable interventions and personalized mental health monitoring. This study aims to investigate the bidirectional time lag relationships between passive GPS data and active ecological momentary assessment (EMA) data collected via smartphone app technology. Between March 2020 and May 2022, we recruited 45 participants (mean age 42.3 years, SD 12.1 years) who were followed up for 6 months: 35 individuals diagnosed with mood disorders referred by psychiatrists and 10 healthy control participants. This resulted in a total of 5248 person-days of data. Over 6 months, we collected 2 types of smartphone data: passive data on movement patterns with nearly 100,000 GPS data points per individual and active data through EMA capturing daily mood levels, including fatigue, irritability, depressed, and manic mood. Our study is limited to Android users due to operating system constraints. Our findings revealed a significant negative correlation between normalized entropy (r=-0.353; P=.04) and weekly depressed mood as well as between location variance (r=-0.364; P=.03) and depressed mood. In participants with mood disorders, we observed bidirectional time-lagged associations. Specifically, changes in homestay were positively associated with fatigue (β=0.256; P=.03), depressed mood (β=0.235; P=.01), and irritability (β=0.149; P=.03). A decrease in location variance was significantly associated with higher depressed mood the following day (β=-0.015; P=.009). Conversely, an increase in depressed mood was significantly associated with reduced location variance the next day (β=-0.869; P<.001). These findings suggest a dynamic interplay between mood symptoms and mobility patterns. This study demonstrates the potential of utilizing active EMA data to assess mood levels and passive GPS data to analyze mobility behaviors, with implications for managing disease progression in patients. Monitoring location variance and homestay can provide valuable insights into this process. The daily use of smartphones has proven to be a convenient method for monitoring patients' conditions. Interventions should prioritize promoting physical movement while discouraging prolonged periods of staying at home.

Estimating descending activation patterns from EMG in fast and slow movements using a model of the stretch reflex.

Due to spinal reflex loops, descending activation from the brain is not the only source of muscle activation that ultimately generates movement. This study directly estimates descending activation patterns from measured patterns of muscle activation (EMG) during human arm movements. A simple model of the spinal stretch reflex is calibrated in a postural unloading task and then used to estimate descending activation patterns from muscle EMG patterns and kinematics during voluntary arm motion performed at different speeds. We observed three key features of the estimated descending activation patterns: (1) Within about the first 15% of movement duration, descending and muscle activations are temporally aligned. Thereafter, they diverge and develop qualitatively different temporal profiles. (2) The time course of descending activation is monotonic for slow movements, non-monotonic for fast movements. (3) Varying model parameters like the spinal reflex gain or the level of co-contraction does not qualitatively change the temporal pattern of estimated descending activation. Our findings highlight the substantial contribution of spinal reflex loops to movement generation, while at the same time providing evidence that the brain must generate qualitatively different descending activation patterns for movements that vary in their mechanical dynamics.

Urban and Groundwater Microplastic Contamination: Sources, Distribution, Impacts, and Remediation Technologies

The presence of microplastics (MPs) in urban environments and groundwater systems has garnered significant global attention due to the critical role groundwater plays as a primary freshwater source. This review paper aims to comprehensively examine the sources, distribution, movement, and environmental impact of MPs, particularly focusing on urban areas and groundwater contamination. Special emphasis is placed on MPs originating from landfill leachate and their distribution along Malaysia's beaches. The paper also discusses the movement patterns of MPs, providing mathematical models for their migration. The environmental and health impacts of MPs, including soil degradation, toxicity in agricultural crops, and heavy metal adsorption, are analyzed. Additionally, current remediation technologies such as reverse osmosis, microbial exploitation, and ozonation are evaluated, with recommendations for combining different methods to enhance MP removal effectiveness. The involvement of the general public, socio-economic sectors, tourism, and waste management companies is highlighted as crucial for addressing this pervasive issue.

Stgcn-pad: a spatial-temporal graph convolutional network for detecting abnormal pedestrian motion patterns at grade crossings

This paper presents a Spatial-Temporal Graph Convolutional Network-based Pedestrians’ behaviors Anomaly Detection system (STGCN-PAD) for grade crossings. The behaviors of pedestrians are represented in a structured manner by skeleton trajectories that are generated using a pose estimation model. The ST-GCN components are sequentially applied to capture the spatial dependencies between skeleton key points within a single video frame and the temporal relationships for each of them. Based on these features, the system reconstructs input trajectories with a constant sliding window size, and the reconstruction error is used to distinguish abnormal behaviors from those normal. To accelerate the processing of extracted multi-dimensional feature maps, an MLP-Mixer model-based reconstruction network is developed as an alternative to the traditional convolution neural network. Only trajectories of normal walking behavior are included for model training. Anomalies, such as lingering and squatting activities, can be identified as outliers by observing the magnitude of reconstruction errors. The case studies demonstrate the salient feasibility and efficiency of the proposed system, which achieves at least comparable performance (approximately 88% in the AUC evaluation metric) with several state-of-the-art approaches while using the MLP-Mixer model accelerates model inference by 10× relative to our previous effort (Song et al. in Appl Intell 53:21676–21691, 2023).

Spinal deformity in a whale shark, Rhincodon typus (Smith 1828), encountered in the northern Gulf of Mexico, with notes on its movement patterns.

This note details the first formal report of a spinal deformation in whale sharks, Rhincodon typus. An individual whale shark with suspected kypholordoscoliosis was observed at Ewing Bank in the Gulf of Mexico during aggregation events in 2010 and 2013. Despite the significant deformity, the shark was observed feeding on fish eggs at the surface during both encounters. Based on satellite tag tracking, its movements, temperature preferences, and depth use were within the range of other whale sharks from the region.

Exploring the Relationship Between Foot Position and Reduced Risk of Knee-Related Injuries in Side-Cutting Movements

Background: Knee-related injuries often result from poor movement patterns that destabilize the joint and increase stress on knee structures. Understanding the influence of foot positioning on knee biomechanics is critical for identifying high-risk movement patterns and preventing injuries. Methods: Twenty healthy male participants performed side-cutting movements at three different foot progression angles. One participant’s data were used to develop and validate a knee finite element model with high-speed dual fluoroscopic imaging (DFIS). Combined with a musculoskeletal analysis, the model simulated internal knee loads under various foot-positioning conditions. Results: The analysis revealed that, as the external foot progression angle increased, the ankle plantarflexion decreased, while the ankle internal rotation and knee valgus moments increased. Higher stress concentrations were observed on the ACL, lateral meniscus, lateral tibial cartilage, and medial collateral ligament, particularly at the femoral–tibial ACL attachments. Conclusion: The findings suggest that a toe-out foot position elevates the risk of knee injuries by increasing stress on key structures, whereas a toe-in position may enhance joint stability, reduce the ACL injury risk, and promote favorable muscle activation patterns.

Migratory Movements: Dissecting the Causes and Patterns from Bihar's Kosi Division

This research paper endeavours to provide a comprehensive analysis of the causes and patterns underlying migratory movements in the Kosi Division of Bihar, India. The Kosi Division, characterized by its unique socio-economic and environmental landscape, has witnessed significant population shifts over the years. This study employs a multi-faceted approach, integrating economic, social, cultural, and environmental factors to unravel the complexities of migration dynamics in the region. Using a combination of quantitative and qualitative methods, the study analyses migration patterns from the Kosi Division, distinguishing between rural and urban shifts. In addition, the research evaluates the role of government policies in shaping migration dynamics, considering the impact of both state and national interventions. The study aims to contribute insights that could inform policy recommendations for addressing the challenges and harnessing the opportunities associated with migration in the Kosi Division. Ultimately, this analytical study seeks to provide a nuanced understanding of the intricate web of causes and patterns of migratory movements in Bihar's Kosi Division, offering valuable insights for academia, policymakers, and development practitioners alike.

Fall Risk Assessment in Active Elderly Through the Use of Inertial Measurement Units: Determining the Right Postural Balance Variables and Sensor Locations

Falls among the elderly have been a significant public health challenge, with severe consequences for individuals and healthcare systems. Traditional balance assessment methods often lack ecological validity, necessitating more comprehensive and adaptable evaluation techniques. This research explores the use of inertial measurement units to assess postural balance in relation to the Berg Balance Scale outcomes. We recruited 14 participants from diverse age groups and health backgrounds, who performed 14 simulated tasks while wearing inertial measurement units on the head, torso, and lower back. Our study introduced a novel metric, i.e., the volume that envelops the 3-dimensional accelerations, calculated as the convex hull space, and used this metric along with others defined in previous studies. Through logistic regression, we demonstrated significant associations between various movement characteristics and the instances of balance loss. In particular, greater movement volume at the lower back (p = 0.021) was associated with better balance, while root-mean-square lower back angular velocity (p = 0.004) correlated with poorer balance. This study revealed that sensor location and task type (static vs. dynamic) significantly influenced the coefficients of the logistic regression model, highlighting the complex nature of balance assessment. These findings underscore the potential of IMUs in providing detailed objective balance assessments in the elderly by identifying specific movement patterns associated with balance impairment across various contexts. This knowledge can guide the development of targeted interventions and strategies for fall prevention, potentially improving the quality of life for older adults.

Neonatal antipredator tactics shape female movement patterns in large herbivores.

Caring for newborn offspring hampers resource acquisition of mammalian females, curbing their ability to meet the high energy expenditure of early lactation. Newborns are particularly vulnerable, and, among the large herbivores, ungulates have evolved a continuum of neonatal antipredator tactics, ranging from immobile hider (such as roe deer fawns or impala calves) to highly mobile follower offspring (such as reindeer calves or chamois kids). How these tactics constrain female movements around parturition is unknown, particularly within the current context of increasing habitat fragmentation and earlier plant phenology caused by global warming. Here, using a comparative analysis across 54 populations of 23 species of large herbivores from 5 ungulate families (Bovidae, Cervidae, Equidae, Antilocapridae and Giraffidae), we show that mothers adjust their movements to variation in resource productivity and heterogeneity according to their offspring's neonatal tactic. Mothers with hider offspring are unable to exploit environments where the variability of resources occurs at a broad scale, which might alter resource allocation compared with mothers with follower offspring. Our findings reveal that the overlooked neonatal tactic plays a key role for predicting how species are coping with environmental variation.

Factors affecting vection and motion sickness in a passive virtual reality driving simulation

The current study sought to examine factors that affect vection (the illusory experience of self-motion in the absence of real motion), visually-induced motion sickness, and one’s sense of presence in a passive virtual reality driving simulation by exposing participants to 60-s pre-recorded driving laps and recording their self-reported metrics as well as their head motion patterns during the laps. Faster virtual driving speed (average 120 mph vs. 60 mph) resulted in significantly higher ratings of vection and motion sickness. Reclined posture (30° back) was examined as a possible mitigating factor for sickness, but no significant effects were found. Expanding visual cues (representing forward self-motion) resulted in higher ratings of vection, motion sickness, and presence compared to contracting cues (representing reverse self-motion) and translational cues (representing lateral self-motion). When experiencing typical upright, world-aligned, forward-facing conditions, conformity to the median head motions along the yaw axis was associated with higher ratings of vection, motion sickness, and presence at slow speeds and with vection and presence at high speeds. These findings underscore the importance of head motion patterns as a metric for behavior and contribute to the general understanding of illusory self-motion perception.

A novel spatio-temporal attention mechanism model for car-following in autonomous driving

Car following is critical for the overall safety, efficiency, and smooth operation of autonomous vehicles in traffic. However, existing car-following model primarily focuses on local feature extraction, overlooking the spatial–temporal relationships between vehicles and key information within the time series. This limitation negatively impacts prediction accuracy and generalization capabilities. To address these issues, a novel Spatio-Temporal Car-Following model based on Graph Convolution Network and Attention mechanism is proposed to enhance the safety, efficiency, and comfort of autonomous driving systems. Firstly, the spatial–temporal structure of the car-following model is constructed using the GCN network. This approach efficiently captures the topological relationships between vehicles. Secondly, we interleave spatial and temporal blocks to extract feature information from both spatial and temporal dimensions, which allows us to perceive spatial interactions and temporal motion patterns of the car. Additionally, a self-attention module assigns different attention weights to the various neighbors of a node in the car-following model, which can facilitate the capture of diverse aspects of node relationships and enhance expressive power. Finally, a Multi-Layer Perceptron (MLP) layer predicts the future behaviors of following vehicles. The proposed car-following model (CF) was trained and evaluated using five real-world datasets: HighD, SPMD, Waymo, NGSIM, and Lyft. The results indicate that our approach surpasses current models in terms of Mean Squared Error (MSE) for spacing, while also maintaining a zero collision rate across every dataset. These findings indicate that the Spatio-Temporal Attention Model (GSTAM-CF) proposed in this paper enhances safety, efficiency, and operational comfort compared to existing models.

Study of a Cable‐Driven Hip Swimming‐Assisted Exoskeleton Utilizing Adaptive Active Control Strategy

ABSTRACTThe power‐assisted exoskeletons have a far‐reaching research significance. The adaptive active control strategy (AACS) of the exoskeleton allows the wearers to achieve personalized assistance tailored to their specific movement patterns and physical needs. However, due to the high requirements for waterproof design and the high difficulty in underwater AACS design, the researches on underwater power‐assisting exoskeletons are hardly to be seen. Therefore, this research proposes a cable‐driven hip swimming‐assisted exoskeleton (SAE) utilizing an AACS. SAE can generate adaptive power‐assisting guiding trajectories of thighs by underwater motion intention perception (UMIP) of the wearer. To achieve UMIP, a deep learning network is presented by combining conventional neural network, bidirectional long short‐term memory network, and attention mechanism (AM‐Dconvbilstm). SAE guides the wearer's thighs to move along the generated guiding trajectory by UMIP through the cables to conduct hip power assisting. The feasibility of the proposed AM‐Dconvbilstm on UMIP and the power‐assisting effect of designed SAE are tested on three elite swimmers by recording corresponding data from IMUs and a sports watch. According to the results of generated trajectories from SAE and actual trajectories from the wearer, the proposed AM‐Dconvbilstm model is verified to predict the wearer's hip motion intention accurately. The power‐assisting effect of SAE is also verified according to the sports watch's heart rate and average energy consumption. All the experiment results greatly validated the feasibility of the proposed AACS and designed SAE.

Tucker Decomposition Enhanced Dynamic Graph Convolutional Networks for Crowd Flows Prediction

Crowd flows prediction is an important problem for traffic management and public safety. Graph Convolutional Network (GCN), known for its ability to effectively capture and utilize topological information, has demonstrated significant advancements in addressing this problem. However, GCN-based models were often based on predefined crowd-flow graphs via historical movement behaviors of human beings and traffic vehicles, which ignored the abnormal changes in crowd flows. In this study, we propose a multi-scale fusion GCN-based framework with Tucker decomposition named mTDNet to enhance dynamic GCN for Crowd flows prediction. Following the paradigm of extant methods, we also employ the predefined crowd-flow graphs as a part of mTDNet to effectively capture the historical movement behaviors of crowd flows. To capture the abnormal changes, we propose a Tucker decomposition-based network with the product of the adjacency matrix of historical movement pattern graphs and an adaptive learning tensor ( \(ALT\) ) by reconstructing the crowd flows. Particularly, we utilize the Tucker decomposition scheme to decompose \(ALT\) , which enhances the dynamic learning of graph structures, allowing for effective capturing of the dynamic changes in crowd flow, including abnormal changes. Furthermore, a multi-scale three-dimensional GCN is utilized to mine and fuse the multiscale spatio-temporal information from crowd flows, to further boost the mTDNet prediction performance. Experiments conducted on two real-world datasets showed that the proposed mTDNet surpasses other crowd flow prediction methods.

The effect of maxillary premolar distalization with different designed clear aligners: a 4D finite element study with staging simulation

BackgroundMolar distalization with clear aligners (CAs) is a common treatment. However, when the molars reach their target position and the distal movement of premolars begins, the mesial movement of molars might reduce the overall efficiency of molar distalization. This study aimed to investigate tooth movement patterns under different CA designs in the premolar distalization stage using a four-dimensional mechanical simulation method.MethodsA finite element method (FEM) model encompassing the maxillary dentition, periodontal ligaments, attachments, and associated CAs was constructed. The simulation aimed to replicate a premolar distalization of 2 mm within 10 sequential steps. Buccal interradicular mini-implants were used. Three groups of CAs were designed: the conventional CA design group (Con group), the second molar half-wrap group (SMHW group) and the all-molar half-wrap group (MHW group). An iterative computational approach was employed to simulate prolonged tooth movement resulting from orthodontic forces. Additionally, morphological alterations in the CA throughout the staging process were simulated utilizing the thermal expansion method.ResultsCompared with the Con and SMHW groups, the MHW group presented significantly reduced mesial movement of the first and second molars. However, the MHW group presented the greatest displacement of canines and incisors. The distalization efficiency of premolars in the MHW group reached 95.5–96.5%, which was substantially greater than that in the Con group (84.5–85%) and the SMHW group (75–75.5%).ConclusionsThe four-dimensional mechanical simulation results indicate that during the process of premolar distalization with CA, removing the distal portion of the aligner covering the first and second molars (MHW group) can effectively reduce the mesial movement of molars. Consequently, this approach can increase the overall efficiency of molar distalization.

Person-specific characteristics of people with low back pain moderate the movement pattern within motor skill training and strength and flexibility exercise: Secondary analysis of a randomized clinical trial

People with low back pain (LBP) display an altered movement pattern during functional activities. Exercise-based treatments improve the altered pattern, but there is individual variability in the response to treatment. This study investigates the moderating effect of person-specific characteristics on a movement pattern at baseline and change over time in the pattern in people with chronic LBP who received motor skill training (MST) or strength and flexibility exercise (SFE). Kinematic data were collected at baseline, immediately post-treatment, and 6 months post-treatment. Lumbar contribution (LC) to total movement was used to quantify the magnitude of the altered movement pattern. Hierarchical linear modeling was used to explore the moderating effects of treatment group, age, sex, duration of LBP, and baseline LC on 1) baseline LC and 2) the change in LC over time. We found that person-specific characteristics moderated the change over time in LC within MST. Older people had a smaller change over time in LC and did not retain the improved pattern as well as younger people (β = 0.01, SE = 0.004, p < 0.01).