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Background: An essential component of childhood development is increasing motor competence. Poor motor learning is often thought to underlie impaired motor competence, but this link is unclear in previous studies. Aims: Our aim was to test the relationship between motor competence and motor learning in the acquisition phase. Both reinforcement learning (RL) and error-based learning (EBL) were tested. We hypothesized that slower RL and slower EBL acquisition rates would relate to lower motor competence. Methods and procedures: Eighty-six participants ages 6-12 performed a target throwing task under RL and EBL conditions. The Movement Assessment Battery for Children - 2nd edition (MABC-2) provided a measure of motor competence. We assessed EBL and RL acquisition rates, baseline variability, and baseline bias from the throwing task. Outcomes and results: In a multiple linear regression model, baseline variability (ß = -0.49, p = <0.001) and the EBL acquisition rate (ß = -0.24, p = 0.018) significantly explained the MABC-2 score. Participants with higher baseline variability and slower EBL acquisition had lower motor competence scores. The RL acquisition rate was independent of MABC-2 score suggesting that RL may be less of a contributor to poor motor competence. Conclusions and implications: Children with slower EBL acquisition had lower motor competence scores but RL acquisition was unrelated to the level of motor competence. Emphasizing the unrelated reinforcement mechanisms over error-based mechanisms during motor skill interventions may help children with poor motor competence better acquire new motor skills.

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Wearable sensors can measure movement in daily life, an outcome that is salient to patients, and have been critical to accelerating progress in rehabilitation research and practice. However, collecting and processing sensor data is burdensome, leaving many scientists with limited access to such data. To address these challenges, we present a harmonized, wearable sensor dataset that combines 2,885 recording days of sensor data from the upper and lower limbs from eight studies. The dataset includes 790 individuals ages 0 - 90, nearly equal sex proportions (53% male, 47% female), and representation from a range of demographic backgrounds (69.4% White, 24.9% Black, 1.8% Asian) and clinical conditions (46% neurotypical, 31% stroke, 7% Parkinson's disease, 6% orthopedic conditions, and others). The dataset is publicly available and accompanied by open source code and an app that allows for interaction with the data. This dataset will facilitate the use of sensor data to advance rehabilitation research and practice, improve the reproducibility and replicability of wearable sensor studies, and minimize costs and duplicated scientific efforts.

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[This corrects the article DOI: 10.3389/fped.2024.1361757.].

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Aim: The rise of wearable sensing technology shows promise for addressing the challenges of measuring motor behavior in pediatric populations. The current pediatric wearable sensing literature is highly variable with respect to the number of sensors used, sensor placement, wearing time, and how data extracted from the sensors are analyzed. Many studies derive conceptually similar variables via different calculation methods, making it hard to compare across studies and clinical populations. In hopes of moving the field forward, this report provides referent upper limb wearable sensor data from accelerometers on 25 variables in typically-developing children, ages 3-17 years. Methods: This is a secondary analysis of data from three pediatric cohorts of children 3-17 years of age. Participants (n = 222) in the cohorts wore bilateral wrist accelerometers for 2-4 days for a total of 622 recording days. Accelerometer data were reprocessed to compute 25 variables that quantified upper limb movement duration, intensity, symmetry, and complexity. Analyses examined the influence of hand dominance, age, gender, reliability, day-to-day stability, and the relationships between variables. Results: The majority of variables were similar on the dominant and non-dominant sides, declined slightly with age, and were not different between boys and girls. ICC values were moderate to excellent. Variation within individuals across days generally ranged from 3% to 32%. A web-based R shiny object is available for data viewing. Interpretation: With the use of wearable movement sensors increasing rapidly, these data provide key, referent information for researchers as they design studies, and analyze and interpret data from neurodevelopmental and other pediatric clinical populations. These data may be of particularly high value for pediatric rare diseases.

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Motor coordination is an important driver of development and improved coordination assessments could facilitate better screening, diagnosis, and intervention for children at risk of developmental disorders. Wearable sensors could provide data that enhance the characterization of coordination and the clinical utility of that data may vary depending on how sensor variables from different recording contexts relate to coordination. We used wearable sensors at the wrists to capture upper-limb movement in 85 children aged 6-12. Sensor variables were extracted from two recording contexts. Structured recordings occurred in the lab during a unilateral throwing task. Unstructured recordings occurred during free-living activity. The objective was to determine the influence of recording context (unstructured versus structured) and assessment type (direct vs. indirect) on the association between sensor variables and coordination. The greatest associations were between six sensor variables from the structured context and the direct measure of coordination. Worse coordination scores were associated with upper-limb movements that had higher peak magnitudes, greater variance, and less smoothness. The associations were consistent across both arms, even though the structured task was unilateral. This finding suggests that wearable sensors could be paired with a simple, structured task to yield clinically informative variables that relate to motor coordination.

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PURPOSE: The purpose of this project was to implement a process for learner-driven, formative, prospective, ad-hoc, entrustment assessment in Doctor of Physical Therapy clinical education. Our goals were to develop an innovative entrustment assessment tool, and then explore whether the tool detected (1) differences between learners at different stages of development and (2) differences within learners across the course of a clinical education experience. We also investigated whether there was a relationship between the number of assessments and change in performance. METHODS: A prospective, observational, cohort of clinical instructors (CIs) was recruited to perform learner-driven, formative, ad-hoc, prospective, entrustment assessments. Two entrustable professional activities (EPAs) were used: (1) gather a history and perform an examination and (2) implement and modify the plan of care, as needed. CIs provided a rating on the entrustment scale and provided narrative support for their rating. RESULTS: Forty-nine learners participated across 4 clinical experiences (CEs), resulting in 453 EPA learner-driven assessments. For both EPAs, statistically significant changes were detected both between learners at different stages of development and within learners across the course of a CE. Improvement within each CE was significantly related to the number of feedback opportunities. CONCLUSION: The results of this pilot study provide preliminary support for the use of learner-driven, formative, ad-hoc assessments of competence based on EPAs with a novel entrustment scale. The number of formative assessments requested correlated with change on the EPA scale, suggesting that formative feedback may augment performance improvement.

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Direct, quantitative measures of hyperactivity and motor coordination, two motor characteristics associated with impairment in autism, are limited. Wearable sensors can objectively index real-world movement variables that may relate to these behaviors. Here, we explored the feasibility of bilateral wrist accelerometers for measuring upper limb activity in 3-10-year-olds with autism (n = 22; 19 boys, 3 girls; M age = 5.64, SD = 2.73 years) and without autism (n = 26; 15 boys, 11 girls; M age = 6.26, SD = 2.47 years). We investigated the relationships between movement characteristics related to duration, intensity, complexity, and symmetry on the one hand and parent-reported hyperactivity and motor coordination on the other. Participants with and without autism wore the sensors for 12-hour periods. Sensor variables varied by age but not sex, with movement intensity and complexity moderately related to motor coordination. These findings lend preliminary support to wearable sensors as a means of providing ecologically-valid metrics of motor characteristics that impact adaptive function in children with autism.

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Background: The use of wearable sensor technology (e.g., accelerometers) for tracking human physical activity have allowed for measurement of actual activity performance of the upper limb (UL) in daily life. Data extracted from accelerometers can be used to quantify multiple variables measuring different aspects of UL performance in one or both limbs. A limitation is that several variables are needed to understand the complexity of UL performance in daily life. Purpose: To identify categories of UL performance in daily life in adults with and without neurological UL deficits. Methods: This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (N=211), two samples of persons with stroke and one sample from neurologically intact adult controls. Data used in these analyses were UL performance variables calculated from accelerometer data, associated clinical measures, and participant characteristics. A total of twelve cluster solutions (3-, 4- or 5-clusters based with 12, 9, 7, or 5 input variables) were calculated to systematically evaluate the most parsimonious solution. Quality metrics and principal component analysis of each solution were calculated to arrive at a locally-optimal solution with respect to number of input variables and number of clusters. Results: Across different numbers of input variables, two principal components consistently explained the most variance. Across the models with differing numbers of UL input performance variables, a 5-cluster solution explained the most overall total variance (79%) and had the best model-fit. Conclusion: The present study identified 5 categories of UL performance formed from 5 UL performance variables in cohorts with and without neurological UL deficits. Further validation of both the number of UL performance variables and categories will be required on a larger, more heterogeneous sample. Following validation, these categories may be used as outcomes in UL stroke research and implemented into rehabilitation clinical practice.

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BACKGROUND AND PURPOSE: This case report describes a decision-making process that was used to progress a home-based intervention that coupled treadmill and walker stepping for a preambulatory toddler with spina bifida. CASE DESCRIPTION: The toddler in this report had an L4-L5 level lesion, and began this home-based intervention at 18 months of age when she was pulling to stand. INTERVENTION: The intervention included parameters for treadmill stepping that prepared this toddler for gait with orthotics and was progressed to overground walking with a walker using a decision-making algorithm based on data obtained from a parent log and coded video. OUTCOMES: This toddler progressed from not stepping at the start of the study to ambulating 150 m with a walker at age 23 months, after 18 weeks of this intervention. DISCUSSION AND CONCLUSION: The intervention and decision-making process used in this study were family centered and may be applicable to gait intervention with other populations.

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