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Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participants level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the bodys center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearsons r = -0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individuals stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Background: Joint range of motion based on the neutral null method, muscle strength based on manual muscle testing, and selective voluntary motor control based on selective control assessment of the lower extremity are standard parameters of a pediatric three-dimensional clinical gait analysis. Lower-limb reference data of children are necessary to identify and quantify abnormalities, but these are limited and when present restricted to specific joints or muscles. Methods: This is the first study that encompasses the aforementioned parameters from a single group of 34 typically developing children aged 5–17 years. Left and right values were averaged for each participant, and then the mean and standard deviation calculated for the entire sample. The data set was tested for statistical significance (p < 0.05). Results: Joint angle reference values are mostly consistent with previously published standards, although there is a large variability in the existing literature. All muscle strength distributions, except for M. quadriceps femoris, differ significantly from the maximum value of 5. The mean number of repetitions of heel-rise test is 12 ± 5. Selective voluntary motor control shows that all distributions, except for M. quadriceps femoris, differ significantly from the maximum value of 2. Conclusion: Since typically developing children do not match expectations and reference values from the available literature and clinical use, this study emphasizes the importance of normative data. Excessively high expectations lead to typically developing children being falsely underestimated and affected children being rated too low. This is of great relevance for therapists and clinicians. Level of evidence: 3. [ABSTRACT FROM AUTHOR]

BACKGROUND: Hand dexterity is important with Duchenne muscular dystrophy (DMD). OBJECTIVE: The aim of this study was to compare hand dexterity and hand laterality task assessments in patients with DMD with typically-developing peers. METHODS: The study included 25 DMD with a mean age of 10.2±2.38 and 21 typically-developing peers with a mean age of 10.33±2.26. Functional levels of DMD patients were determined by Brooke Upper Extremity Functional Classification Scale and Brooke Lower Extremity Functional Scale. The ABILHAND-Kids and 9-hole peg test were used to assess the hand dexterity of all participants, and assess the hand laterality task. RESULTS: Patients with DMD had lower ABILHAND-Kids scores than their typically-developing peers (p < 0.001). Patients with DMD had higher 9-hole peg test duration on the dominant and non-dominant extremity compared to typically-developing peers (p < 0.001). Patients with DMD were found to be different from their typically-developing peers (p < 0.001) in lateralization response time and accuracy. CONCLUSION: Patients with DMD were found to have lower manual dexterity and hand laterality task skills compared to their typically-developing peers. It is recommended that hand dexterity and upper extremity recognition capacities should be considered in assessment and intervention programs for physiotherapists and clinicians working in this field. [ABSTRACT FROM AUTHOR]

Introduction: Children with neurogenetic syndromes commonly experience significant and pervasive sleep disturbances, however, associations with caregiver mental health remains unclear. Previous studies have linked sleep disturbances with increased caregiver depression in typically developing populations, and heightened caregiver stress among neurogenetic populations. The present study expands on findings by exploring the longitudinal association between child sleep duration and caregiver mental health (depression, anxiety, stress) throughout development (infancy to school-aged children) in dyads with and without a child affected by a neurogenetic syndrome. Methods: Participants were drawn from the Purdue Early Phenotype Study, including 193 caregivers (Age: M = 34.40 years, SD = 4.53) of children with neurogenetic syndromes (Age: M = 40.91 months, SD =20.72) and typically developing children (n = 55; Age: M = 36.71 months, SD = 20.68). Children in the neurogenetic group were diagnosed with Angelman (n = 49), Prader Willi (n = 30), Williams (n = 51), and Fragile X (n = 8) syndromes. Caregivers completed assessments every six months up to child age three, and annual assessments thereafter. Child sleep duration was measured using the Brief Infant Sleep Questionnaire, and caregiver internalizing symptoms were assessed using the Depression, Anxiety, Stress Scale. Multilevel models were conducted to examine caregiver depression, anxiety, and stress in relation to child sleep duration at both between- and within-person levels, with child age as a moderator. Results: Results indicated a between-person effect of child sleep duration on caregiver depression (i.e., differences between families) and a within-person effect on caregiver stress (i.e., change over time) in the full, combined sample. These effects were not maintained when examined separately in neurogenetic and typically developing groups, except for a between-person effect on caregiver stress in the typically developing cohort. Moderating effects of child age were significant for depression and stress only in the typically developing cohort. Discussion: In summary, persistent child sleep disruptions were linked to exacerbated caregiver depression across the sample, while acute child sleep disruptions exacerbate caregiver stress within dyads over time. These findings emphasize the importance of addressing child sleep to enhance caregiver wellbeing and has potential relevance for a wide range of neurogenetic syndromes. [ABSTRACT FROM AUTHOR]

The cerebellum contributes to motor and higher-order control throughout neurodevelopment, with marked growth during childhood. Few studies have investigated differential associations of cerebellar morphometry with function in males and females. The present study examines sex differences in regional cerebellar gray matter volume (GMV) and the moderating effect of sex on the relationship between GMV and motor, cognitive, and emotional functions in a large cohort of typically developing (TD) children. Participants included 371 TD children (123 females, age 8–12 years). A convolutional neural network-based approach was employed for cerebellar parcellation. Volumes were harmonized using ComBat to adjust for hardware-induced variations. Regression analyses examined the effect of sex on GMV and whether sex moderated the relationship between GMV and motor, cognitive, and emotional functions. Males showed larger GMV in right lobules I–V, bilateral lobules VI, crus II/VIIb, and VIII, left lobule X, and vermis regions I–V and VIII–X. Greater motor function correlated with less vermis VI–VII GMV in females. Greater cognitive function correlated with greater left lobule VI GMV in females and less left lobule VI GMV in males. Finally, greater internalizing symptoms correlated with greater bilateral lobule IX GMV in females but less in males. These findings reveal sexually dimorphic patterns of cerebellar structure and associations with motor, cognitive, and emotional functions. Males generally show larger GMV than females. Larger GMV was associated with better cognitive functioning for females and better motor/emotional functioning for males. [ABSTRACT FROM AUTHOR]

Detection of Autism Spectrum Disorders (ASD) remains challenging due to complex psychiatric symptoms and limited neurobiological evidence. To address this, an automatic model is developed in this study for detecting ASD and Typically Developing (TD) individuals. The Autism Brain Imaging Data Exchange (ABIDE) dataset, which includes structural Magnetic Resonance Imaging (sMRI), is used in this study for model evaluation. The proposed methodology consists of two main stages: preprocessing and model optimization. In the preprocessing stage, unclear images are discarded, and edges are detected using the Canny Deriche Edge Detection (CDED) algorithm. Subsequently, images are resized, and data augmentation techniques are applied. The Deep Convolutional Neural Networks (DCNN) are fine-tuned using the Dipper-Throated Particle Swarm Optimization (DTPSO) algorithm. Additionally, Class Activation Mapping (Grad-CAM) is utilized to visualize and interpret the model’s decision-making process. The proposed model has shown the highest performance with a 95.9% accuracy, 96.5% precision, 95.9% sensitivity, 95.9% specificity, a 96.2% F1-score, and a 94.5% AUC. These results validate the effectiveness of the proposed CDED-DCNN-DTPSO approach in detecting ASD compared to the DCNN-DTPSO model. The proposed model demonstrates higher accuracy, precision, sensitivity, specificity, F1-score, and AUC, indicating its performance in identifying ASD.

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant's level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body's center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson's r = −0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual's stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers. [ABSTRACT FROM AUTHOR]

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens. [ABSTRACT FROM AUTHOR]

IntroductionChildren with neurogenetic syndromes commonly experience significant and pervasive sleep disturbances, however, associations with caregiver mental health remains unclear. Previous studies have linked sleep disturbances with increased caregiver depression in typically developing populations, and heightened caregiver stress among neurogenetic populations. The present study expands on findings by exploring the longitudinal association between child sleep duration and caregiver mental health (depression, anxiety, stress) throughout development (infancy to school-aged children) in dyads with and without a child affected by a neurogenetic syndrome.MethodsParticipants were drawn from the Purdue Early Phenotype Study, including 193 caregivers (Age: M = 34.40 years, SD = 4.53) of children with neurogenetic syndromes (Age: M = 40.91 months, SD =20.72) and typically developing children (n = 55; Age: M = 36.71 months, SD = 20.68). Children in the neurogenetic group were diagnosed with Angelman (n = 49), Prader Willi (n = 30), Williams (n = 51), and Fragile X (n = 8) syndromes. Caregivers completed assessments every six months up to child age three, and annual assessments thereafter. Child sleep duration was measured using the Brief Infant Sleep Questionnaire, and caregiver internalizing symptoms were assessed using the Depression, Anxiety, Stress Scale. Multilevel models were conducted to examine caregiver depression, anxiety, and stress in relation to child sleep duration at both between- and within-person levels, with child age as a moderator.ResultsResults indicated a between-person effect of child sleep duration on caregiver depression (i.e., differences between families) and a within-person effect on caregiver stress (i.e., change over time) in the full, combined sample. These effects were not maintained when examined separately in neurogenetic and typically developing groups, except for a between-person effect on caregiver stress in the typically developing cohort. Moderating effects of child age were significant for depression and stress only in the typically developing cohort.DiscussionIn summary, persistent child sleep disruptions were linked to exacerbated caregiver depression across the sample, while acute child sleep disruptions exacerbate caregiver stress within dyads over time. These findings emphasize the importance of addressing child sleep to enhance caregiver wellbeing and has potential relevance for a wide range of neurogenetic syndromes.

Autism spectrum disorder (ASD) poses a complex challenge to researchers and practitioners, with its multifaceted etiology and varied manifestations. Timely intervention is critical in enhancing the developmental outcomes of individuals with ASD. This paper underscores the paramount significance of early detection and diagnosis as a pivotal precursor to effective intervention. To this end, integrating advanced technological tools, specifically eye-tracking technology and deep learning algorithms, is investigated for its potential to discriminate between children with ASD and their typically developing (TD) peers. By employing these methods, the research aims to contribute to refining early detection strategies and support mechanisms. This study introduces innovative deep learning models grounded in convolutional neural network (CNN) and recurrent neural network (RNN) architectures, employing an eye-tracking dataset for training. Of note, performance outcomes have been realised, with the bidirectional long short-term memory (BiLSTM) achieving an accuracy of 96.44%, the gated recurrent unit (GRU) attaining 97.49%, the CNN-LSTM hybridising to 97.94%, and the LSTM achieving the most remarkable accuracy result of 98.33%. These outcomes underscore the efficacy of the applied methodologies and the potential of advanced computational frameworks in achieving substantial accuracy levels in ASD detection and classification. [ABSTRACT FROM AUTHOR]

In a relaxed and awake state with the eyes closed, 8–12 Hz neural oscillations are the dominant rhythm, most prominent in parietal‐occipital regions. Resting‐state (RS) alpha is associated with processing speed and is also thought to be central to how networks process information. Unfortunately, the RS eyes‐closed (EC) exam can only be used with individuals who can remain awake with their eyes closed for an extended period. As such, infants, toddlers, and individuals with intellectual disabilities are usually excluded from RS alpha studies. Previous research suggests obtaining RS alpha measures in a dark room with the eyes open as a viable alternative to the traditional RS EC exam. To further explore this, RS EC and RS dark room (DR) eyes‐open alpha activity was recorded using magnetoencephalography in children with typical development (TD; N = 37) and children with autism spectrum disorder (ASD; N = 30) 6.9–12.6 years old. Findings showed good reliability for the RS EC and DR peak alpha frequency (frequency with strongest alpha power; interclass correlation (ICC) = 0.83). ICCs for posterior alpha power were slightly lower (ICCs in the 0.70 s), with an ~ 5% reduction in posterior alpha power in the DR than EC condition. No differences in the EC and DR associations were observed between the TD and ASD groups. Finally, age was associated with both EC and DR peak alpha frequency. Findings thus indicate the DR exam as a viable way to obtain RS alpha measures in populations frequently excluded from electrophysiology RS studies. [ABSTRACT FROM AUTHOR]

This mixed method study examined maternal perceptions of their children's sibling relationship when one child has autism spectrum disorder (ASD) and one is typically developing. Six mothers completed a questionnaire and semi-structured interview pertaining to their perceptions of the quality of the sibling relationship, the children's development, adjustment, and impact on one another. Mothers spoke kindly of both children and offered positive and negative perceptions of the sibling relationship. Mothers perceived that sibling influence was bi-directional, in that both children affect the behaviors and interests of one another, regardless of diagnosis and birth order. Typically developing siblings were described as very involved and caring towards their sibling; however, siblings and parents seem to experience day-to-day challenges associated with family relationships and dynamics. Findings highlight the need for services to address the individual needs of parents and siblings in response to having a child with ASD. Highlights: Mother's perception of the sibling relationship quality was investigated and revealed positive and negative dynamics between siblings with ASD and typically developing siblings. According to mothers, there is evidence of a bi-directional influence on each child's personality and development when one sibling has ASD. Developmental age may allow the typically developing sibling to take on certain roles, regardless of birth order. [ABSTRACT FROM AUTHOR]

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant’s level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body’s center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson’s r = −0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual’s stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers.

Autism spectrum disorder (ASD) poses a complex challenge to researchers and practitioners, with its multifaceted etiology and varied manifestations. Timely intervention is critical in enhancing the developmental outcomes of individuals with ASD. This paper underscores the paramount significance of early detection and diagnosis as a pivotal precursor to effective intervention. To this end, integrating advanced technological tools, specifically eye-tracking technology and deep learning algorithms, is investigated for its potential to discriminate between children with ASD and their typically developing (TD) peers. By employing these methods, the research aims to contribute to refining early detection strategies and support mechanisms. This study introduces innovative deep learning models grounded in convolutional neural network (CNN) and recurrent neural network (RNN) architectures, employing an eye-tracking dataset for training. Of note, performance outcomes have been realised, with the bidirectional long short-term memory (BiLSTM) achieving an accuracy of 96.44%, the gated recurrent unit (GRU) attaining 97.49%, the CNN-LSTM hybridising to 97.94%, and the LSTM achieving the most remarkable accuracy result of 98.33%. These outcomes underscore the efficacy of the applied methodologies and the potential of advanced computational frameworks in achieving substantial accuracy levels in ASD detection and classification.

Background: Specific language impairment (SLI) is described as a heterogeneous deficit that causes difficulties in various aspects of language. We performed a comparative study of two methods of language assessment with the primary objective of determining the most effective approach for identifying adolescents with syntactic SLI and typical development (TD) in use. Methods: A software-assisted method using E-Prime 2.0 was used to create an experiment. The participants were Malay adolescents aged 13 years old-15 years old. The conventional method was compared with the software-assisted method to assess the participants' comprehension and production performance. Data on reaction time (RT), scoring and no response (NR) were obtained from the adolescents. Results: Based on the two methods, the findings on the selection of participants for the SLI and TD groups was different. The two methods produced similar results in terms of the selection of TD group and most participants in the syntactic SLI group except for two participants who failed in the conventional method but passed the test in the software-assisted method. Conclusion: The descriptive evaluation of the findings suggested selecting softwareassisted method as the alternative source because the provided information was detailed and this information enabled the researcher to identify the SLI group. [ABSTRACT FROM AUTHOR]

During childhood, play contributes to the physical, emotional, cognitive and social development of infants and children and may enhance future mental health. The aim of this study was to examine the relationship between play performance factors and psychosocial problems in school-aged children. A total of 142 typical Spanish children aged 5 to 9 years were included. Play performance was measured with the My Child's Play questionnaire, while the Strengths and Difficulties Questionnaire was used to evaluate internalizing and externalizing problems. The findings showed that personal, environmental and activity factors of play performance were associated with psychosocial problems and prosocial behavior in children. Moreover, children with high psychosocial difficulties reported significantly poorer play performance. As executive functioning during play was the factor that was most strongly associated with internalizing and externalizing psychosocial difficulties, it is possible that executive functions have a decisive role on both social cognition and self-regulation during play performance. [ABSTRACT FROM AUTHOR]

Purpose: The current study was designed to investigate and compare the feeding practices across three districts of Maharashtra and across maternal literacy levels. Materials and Methods: This study employed a cross-sectional survey. A survey tool was developed to assess the feeding practices, which included items related to the hierarchy of food types introduced to infants, their consistency, the age of introduction of various foods, utensils used, and position used to feed the infants. The survey tool was validated by experienced speech-language pathologists (SLPs). Ninety-five mothers of children in the age range of 1-3 years (30 mothers from each of the two districts-Nagpur, Sangli, and 35 participants from Mumbai) were included as participants. The participants of all the districts were also grouped based on their literacy levels. The survey tool was administered via online mode. Results: This study revealed that there were many differences in feeding practices across districts and literacy levels. Only the utensil used to provide complementary feeds, consistency of the food introduced after complementary feeds, and the position used to feed the foods of next consistency were similar across districts. Across literacy levels, only the consistency of complementary feeds, consistency of the food introduced after complementary feeds, position to feed the next consistency, utensil and position used to feed water were similar. All the other feeding practices varied across districts and literacy levels. Conclusion: It can be concluded that the geographical location of the districts across Maharashtra and maternal literacy levels influenced feeding practices. The findings of this study imply that the practicing SLPs should consider the variations in feeding practices that exist across districts during the assessment and intervention of young children with feeding issues. [ABSTRACT FROM AUTHOR]

Background: Robotic devices have been used to quantify function, identify impairment, and rehabilitate motor function extensively in adults, but less-so in younger populations. The ability to perform motor actions improves as children grow. It is important to quantify this rate of change of the neurotypical population before attempting to identify impairment and target rehabilitation techniques. Objectives: For a population of typically developing children, this systematic review identifies and analyzes tools and techniques used with robotic devices to quantify upper-limb motor function. Since most of the papers also used robotic devices to compare function of neurotypical to pathological populations, a secondary objective was introduced to relate clinical outcome measures to identified robotic tools and techniques. Methods: Five databases were searched between February 2019 and August 2020, and 226 articles were found, 19 of which are included in the review. Results: Robotic devices, tasks, outcome measures, and clinical assessments were not consistent among studies from different settings but were consistent within laboratory groups. Fifteen of the 19 articles evaluated both typically developing and pathological populations. Conclusion: To optimize universally comparable outcomes in future work, it is recommended that a standard set of tasks and measures is used to assess upper-limb motor function. Standardized tasks and measures will facilitate effective rehabilitation. [ABSTRACT FROM AUTHOR]

Background: Walking problems in children with cerebral palsy (CP) can in part be explained by limited selective motor control. Muscle synergy analysis is increasingly used to quantify altered neuromuscular control during walking. The early brain injury in children with CP may lead to a different development of muscle synergies compared to typically developing (TD) children, which might characterize the abnormal walking patterns.Objective: The overarching aim of this review is to give an overview of the existing studies investigating muscle synergies during walking in children with CP compared to TD children. The main focus is on how muscle synergies differ between children with CP and TD children, and we examine the potential of muscle synergies as a measure to quantify and predict treatment outcomes.Methods: Bibliographic databases were searched by two independent reviewers up to 22 April 2019. Studies were included if the focus was on muscle synergies of the lower limbs during walking, obtained by a matrix factorization algorithm, in children with CP.Results: The majority (n = 12) of the 16 included studies found that children with CP recruited fewer muscle synergies during walking compared to TD children, and several studies (n = 8) showed that either the spatial or temporal structure of the muscle synergies differed between children with CP and TD children. Variability within and between subjects was larger in children with CP than in TD children, especially in more involved children. Muscle synergy characteristics before treatments to improve walking function could predict treatment outcomes (n = 3). Only minimal changes in synergies were found after treatment.Conclusions: The findings in this systematic review support the idea that children with CP use a simpler motor control strategy compared to TD children. The use of muscle synergy analysis as a clinical tool to quantify altered neuromuscular control and predict clinical outcomes seems promising. Further investigation on this topic is necessary, and the use of muscle synergies as a target for development of novel therapies in children with CP could be explored.

Eye-tracking research on social attention in infants and toddlers has included heterogeneous stimuli and analysis techniques. This allows measurement of looking to inner facial features under diverse conditions but restricts across-study comparisons. Eye–mouth index (EMI) is a measure of relative preference for looking to the eyes or mouth, independent of time spent attending to the face. The current study assessed whether EMI was more robust to differences in stimulus type than percent dwell time (PDT) toward the eyes, mouth, and face. Participants were typically developing toddlers aged 18–30 months (N = 58). Stimuli were dynamic videos with single and multiple actors. It was hypothesized that stimulus type would affect PDT to the face, eyes, and mouth, but not EMI. Generalized estimating equations demonstrated that all measures including EMI were influenced by stimulus type. Nevertheless, planned contrasts suggested that EMI was more robust than PDT when comparing heterogeneous stimuli. EMI may allow for a more robust comparison of social attention to inner facial features across eye-tracking studies. [ABSTRACT FROM AUTHOR]

The aim of this study was to outline an age-dependent range of posturography measures obtained from healthy children. One hundred and fifty healthy 4-18-year-old children underwent video Head Impulse Test and static posturography testing. Surface, length and power spectra values were compared between each group of pupils and 32 healthy adults. As well as a significant (p < 0.025) increase in surface and length, when compared to healthy adults, increased values within the low/middle frequency domain and within the high frequency domain were also observed in 4-13 and 4-7-year-old children, respectively. In conclusion, although the nature of postural control development remains largely undetermined, this study represents a first attempt to outline an age-dependent normal range of the relative contribution of inputs in typically developing children. [ABSTRACT FROM AUTHOR]

Background: Walking problems in children with cerebral palsy (CP) can in part be explained by limited selective motor control. Muscle synergy analysis is increasingly used to quantify altered neuromuscular control during walking. The early brain injury in children with CP may lead to a different development of muscle synergies compared to typically developing (TD) children, which might characterize the abnormal walking patterns. Objective: The overarching aim of this review is to give an overview of the existing studies investigating muscle synergies during walking in children with CP compared to TD children. The main focus is on how muscle synergies differ between children with CP and TD children, and we examine the potential of muscle synergies as a measure to quantify and predict treatment outcomes. Methods: Bibliographic databases were searched by two independent reviewers up to 22 April 2019. Studies were included if the focus was on muscle synergies of the lower limbs during walking, obtained by a matrix factorization algorithm, in children with CP. Results: The majority (n = 12) of the 16 included studies found that children with CP recruited fewer muscle synergies during walking compared to TD children, and several studies (n = 8) showed that either the spatial or temporal structure of the muscle synergies differed between children with CP and TD children. Variability within and between subjects was larger in children with CP than in TD children, especially in more involved children. Muscle synergy characteristics before treatments to improve walking function could predict treatment outcomes (n = 3). Only minimal changes in synergies were found after treatment. Conclusions: The findings in this systematic review support the idea that children with CP use a simpler motor control strategy compared to TD children. The use of muscle synergy analysis as a clinical tool to quantify altered neuromuscular control and predict clinical outcomes seems promising. Further investigation on this topic is necessary, and the use of muscle synergies as a target for development of novel therapies in children with CP could be explored. [ABSTRACT FROM AUTHOR]

This systematic review examined to what extent response demands of executive functioning (EF) tasks influence the relationship between motor performance and EF in 2- to 6-year-old typically developing (TD) children and children with motor coordination difficulties (MCD). Eighteen of the included articles focused on TD children only and three also on children with MCD. EF tasks were subdivided based on the type of responses (i.e., motor or verbal). EF tasks requiring a motor response were subdivided into two levels (i.e., complex or simple). Results showed that the relationship between motor performance and EF in 2- to 6-year-old TD children and children with MCD was inconclusive with the strength of correlation coefficients for the most part varying from very weak to moderate. The type of EF task response did not influence the relationship between motor performance and EF. The review thus implies that other task demands than the type of EF task response need to be investigated to explain the inconsistent relationship between motor performance and EF, such as the complexity of the motor response of EF tasks, the complexity of the actual motor tasks, and additional processes (e.g., memory, intelligence, language comprehension).

Highlights • Clinicians use foot pressure normative data for comparison with pathology. • Foot pressure technology, collection procedures and post-processing vary. • Previously published normative foot pressure data is summarized. • Standardization in data collection and post processing procedures is needed. Abstract Foot pressure analysis is a valuable tool that can be used to quantify foot function in children. For researchers and clinicians who use foot pressure analysis, data from a typically developing population is used for comparison with subjects that have musculoskeletal conditions. However, differences between foot pressure data collection technology, data collection procedures and post-processing techniques make comparisons between devices, as well as direct comparisons of pediatric foot pressure data, difficult. When comparing data from multiple studies it is imperative that the studies utilize the same data collection and processing techniques, otherwise the data should not be directly compared. The purpose of this paper is to provide a summary of typically developing foot pressure data, explore factors that affect foot pressure data collection and provide suggestions for the standardization of foot pressure data collection and post processing. The results of this review demonstrate that the minimum data collection and processing recommendations for foot pressure data collection and post-processing are: using a midgait or two-step approach, allowing subjects to walk at their self-selected speed, collecting a minimum of three trials per foot, identifying at minimum medial and lateral hindfoot, forefoot, midfoot, the hallux and toes, and that parameters be reported in standard units. [ABSTRACT FROM AUTHOR]