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Complex motor commands for human locomotion are generated through the combination of motor modules representable as muscle synergies. Recent data have argued that muscle synergies are inborn or determined early in life, but development of the neuro-musculoskeletal system and acquisition of new skills may demand fine-tuning or reshaping of the early synergies. We seek to understand how locomotor synergies change during development and training by studying the synergies for running in preschoolers and diverse adults from sedentary subjects to elite marathoners, totaling 63 subjects assessed over 100 sessions. During development, synergies are fractionated into units with fewer muscles. As adults train to run, specific synergies coalesce to become merged synergies. Presences of specific synergy-merging patterns correlate with enhanced or reduced running efficiency. Fractionation and merging of muscle synergies may be a mechanism for modifying early motor modules (Nature) to accommodate the changing limb biomechanics and influences from sensorimotor training (Nurture). Motor commands for human locomotion are generated by combination of muscle synergies. In humans, muscle synergies for running exhibit considerable plasticity during child-to-adult development and adult training to meet the constantly changing biomechanical and efficiency demands.

Scoliosis in skeletally immature children is often treated by implantation of a rod to straighten the spine. Rods can be distracted (lengthened) as the spine grows, but patients need many invasive operations under general anaesthesia. Such operations are costly and associated with negative psychosocial outcomes. We assessed the effectiveness and safety of a new magnetically controlled growing rod (MCGR) for non-invasive outpatient distractions. We implanted the MCGR in five patients, two of whom have now reached 24 months' follow-up. Each patient underwent monthly outpatient distractions. We used radiography to measure the magnitude of the spinal curvature, rod distraction length, and spinal length. We assessed clinical outcome by measuring the degree of pain, function, mental health, satisfaction with treatment, and procedure-related complications. In the two patients with 24 months' follow-up, the mean degree of scoliosis, measured by Cobb angle, was 67° (SD 10°) before implantation and 29° (4°) at 24 months. Length of the instrumented segment of the spine increased by a mean of 1·9 mm (0·4 mm) with each distraction. Mean predicted versus actual rod distraction lengths were 2·3 mm (1·2 mm) versus 1·4 mm (0·7 mm) for patient 1, and 2·0 mm (0·2 mm) and 2·1 mm (0·7 mm) versus 1·9 mm (0·6 mm) and 1·7 mm (0·8 mm) for patient 2's right and left rods, respectively. Throughout follow-up, both patients had no pain, had good functional outcome, and were satisfied with the procedure. No MCGR-related complications were noted. The MCGR procedure can be safely and effectively used in outpatient settings, and minimises surgical scarring and psychological distress, improves quality of life, and is more cost-effective than is the traditional growing rod procedure. The technique could be used for non-invasive correction of abnormalities in other disorders. Ellipse Technologies.

Abstract Introduction: Obesity is a public health crisis in the USA. This study aimed to estimate the prevalence of obesity and severe obesity in US children and adolescents and identify novel targetable risk factors associated with childhood obesity. Methods: From the US National Health and Nutrition Examination Survey from 1999 to 2018, 35,907 children aged 2–19 with body mass index (BMI) data were included. Obesity and severe obesity were defined as BMI ≥95th percentile and ≥120% of 95th percentile of US Centers for Disease Control and Prevention growth charts, respectively. Trends in the prevalence of obesity and subgroup analyses according to socioeconomic factors and language used in the interview were analyzed. Results: The prevalence of obesity and severe obesity increased from 14.7 [95% confidence interval: 12.9–17.0]% to 19.2 [17.2–21.0]% and 3.9 [2.9–5.0]% to 6.1 [4.8–8.0]% in 1999–2018, respectively (p = 0.001 and p = 0.014, respectively). In 2017–2018, the prevalence of obesity among children from Spanish-speaking households was 24.4 [22.4–27.0]%, higher than children from English-speaking households (p = 0.027). Conclusion: The prevalence of childhood obesity kept increasing in 1999–2018. The problem is worse in children from Spanish-speaking households. Novel and targeted public health intervention strategies are urgently warranted to effectively halt the rising epidemic of childhood obesity.

Emerging research is highlighting the importance of fostering artificial intelligence (AI) literacy among educated citizens of diverse academic backgrounds. However, what to include in such literacy programmes and how to teach literacy is still under-explored. To fill this gap, this study designed and evaluated an AI literacy programme based on a multi-dimensional conceptual framework, which developed participants' conceptual understanding, literacy, empowerment and ethical awareness. It emphasised conceptual building, highlighted project work in application development and initiated teaching ethics through application development. Thirty-six university students with diverse academic backgrounds joined and completed this programme, which included 7 hours on machine learning, 9 hours on deep learning and 14 hours on application development. Together with the project work, the results of the tests, surveys and reflective writings completed before and after these courses indicate that the programme successfully enhanced participants' conceptual understanding, literacy, empowerment and ethical awareness. The programme will be extended to include more participants, such as senior secondary school students and the general public. This study initiates a pathway to lower the barrier to entry for AI literacy and addresses a public need. It can guide and inspire future empirical and design research on fostering AI literacy among educated citizens of diverse backgrounds.

Identification of runner’s performance level is critical to coaching, performance enhancement and injury prevention. Machine learning techniques have been developed to measure biomechanical parameters with body-worn inertial measurement unit (IMU) sensors. However, a robust method to classify runners is still unavailable. In this paper, we developed two models to classify running performance and predict biomechanical parameters of 30 subjects. We named the models RunNet-CNN and RunNet-MLP based on their architectures: convolutional neural network (CNN) and multilayer perceptron (MLP), respectively. In addition, we examined two validation approaches, subject-wise (leave-one-subject-out) and record-wise. RunNet-MLP classified runner’s performance levels with an overall accuracy of 97.1%. Our results also showed that RunNet-CNN outperformed RunNet-MLP and gradient boosting decision tree in predicting biomechanical parameters. RunNet-CNN showed good agreement (R2 > 0.9) with the ground-truth reference on biomechanical parameters. The prediction accuracy for the record-wise method was better than the subject-wise method regardless of biomechanical parameters or models. Our findings showed the viability of using IMUs to produce reliable prediction of runners’ performance levels and biomechanical parameters.

This is a prospective study to establish prediction models that map the refined Scoliosis Research Society 22-item (SRS-22r) onto EuroQoL-5 dimension 5-level (EQ-5D-5L) utility scores in adolescent idiopathic scoliosis (AIS) patients. Comparison of treatment outcomes in AIS can be determined by cost-utility analysis. However, the mainstay spine-specific health-related quality of life outcome measure, the SRS-22r questionnaire does not provide utility assessment. In this study, AIS patients were prospectively recruited to complete both the EQ-5D-5L and SRS-22r questionnaires by trained interviewers. Ordinary least squares regression was undertaken to develop mapping models, which the validity and robustness were assessed by using the 10-fold cross-validation procedure. EQ-5D-5L utility scores were regressed on demographics, Cobb angle, curve types, treatment modalities, and five domains of the SRS-22r questionnaire. Three models were developed using stepwise selection method. EQ-5D-5L scores were regressed on 1) main effects of SRS-22r subscale scores, 2) as per 1 plus squared and interaction terms, and 3) as per 2 plus demographic and clinical characteristics. Model goodness-of-fit was assessed using R-square, adjusted R-square, and information criteria; whereas the predictive performance was evaluated using root mean square error (RMSE), mean absolute error (MAE), and the proportion of absolute error within the threshold of 0.05 and 0.10. A total of 227 AIS patients with mean age of 15.6 years were recruited. The EQ-5D-5L scores were predicted by four domains of SRS-22r (main effects of 'Function', 'Pain', 'Appearance' and 'Mental Health', and squared term of 'Function' and 'Pain'), and Cobb angle in Model 3 with the best goodness-of-fit (R-square/adjusted R-square: 62.1%/60.9%). Three models demonstrated an acceptance predictive performance in error analysis applying 10-fold cross-validation to three models where RMSE and MAE were between 0.063-0.065 and between 0.039-0.044, respectively. Model 3 was therefore recommended out of three mapping models established in this paper. To our knowledge, this is the first study to map a spine-specific health-related quality of life measure onto EQ-5D-5L for AIS patients. With the consideration and incorporation of demographic and clinical characteristics, over 60% variance explained by mapping model 3 enabled the satisfactory prediction of EQ-5D-5L utility scores from existing SRS-22r data for health economic appraisal of different treatment options.

In this study we modelled possible causes and consequences of student burnout and engagement on academic efficacy and dropout intention in university students. Further we asked, can student engagement protect against the effects of burnout? In total 4,061 university students from Portugal, Brazil, Mozambique, the United Kingdom, the United States of America, Finland, Serbia, and Macao SAR, Taiwan participated in this study. With the data collected we analyzed the influence of Social Support, Coping Strategies, and school/course related variables on student engagement and burnout using structural equation modeling. We also analyzed the effect of student engagement, student burnout, and their interaction, on Academic Performance and Dropout Intention. We found that both student engagement and burnout are good predictors of subjective academic performance and dropout intention. However, student burnout suppresses the effect of student engagement on these variables. This result has strong implications for practitioners and administrators. To prevent student dropout, it is not enough to promote student engagement-additionally, and importantly, levels of student burnout must be kept low. Other variables such as social support and coping strategies are also relevant predictors of student engagement and burnout and should be considered when implementing preventive actions, self-help and guided intervention programs for college students.

Current clinical evidence suggests that a well-planned physiotherapeutic scoliosis specific exercise (PSSE) program is effective for scoliosis regression. We investigated the effect of curve patterns on Cobb angles with PSSE. This was a non-randomized prospective clinical trial that recruited participants with adolescent idiopathic scoliosis between January and June 2017. Participants were grouped by curve pattern into major thoracic and major lumbar groups. An outpatient-based PSSE program was conducted with the following schedule of intensive exercise: ≥ 1 session of supervised PSSE per month and > 30min of home exercise 5 days/week in the first 6 months, after which exercise frequency was reduced to 1 session of supervised PSSE every three months and > 30min of home exercise 5 days/week until 2 years after study initiation. Radiographic Cobb angle progressions were identified at the 1, 1.5 and 2-year follow-ups. A mixed model analysis of variance (ANOVA) was performed to examine the differences in Cobb angles between groups at four testing time points. The two-tailed significance level was set to 0.05. In total, 40 participants were recruited, including 22 with major thoracic curves (5 males and 17 females; mean age 13.5±1.8 years; Cobb angle 18-45 degrees) and 18 with major lumbar curves (7 males and 11 females; mean age 12.7±1.7 years; Cobb angle 15-48 degrees). Curve regressions, namely the reduction of Cobb angles between 7 to 10 degrees were noted in 9.1% of participants in the major thoracic group; reductions of 6 to 13 degrees were noted in 33.3% of participants in the major lumbar group at the 2-year follow-up. Repeated measurements revealed a significant time effect (F2.2,79.8 = 4.1, p = 0.02), but no group (F2.2,79.8 = 2.3, p = 0.1) or time × group (F1,37 = 0.97, p = 0.3) effects in reducing Cobb angles after 2 years of PSSE. A logistic regression analysis revealed that no correlation was observed between curve pattern and curve regression or stabilization (OR: 0.2, 95% CI: 0.31-1.1, p = 0.068) at the 2-year follow-up. This was the first study to investigate the long-term effects of PSSE in reducing Cobb angles on the basis of major curve location. No significant differences in correction were observed between major thoracic and major lumbar curves. A regression effect and no curve deterioration were noted in both groups at the 2-year follow-up. ChiCTR1900028073.