Two-year motor outcomes associated with the dose of NICU based physical therapy: The Noppi RCT

Background - Interventions involving both the parent and the preterm infant have demonstrated lasting effects on cognitive outcomes, but motor effects are less salient. It remains unclear when to commence early intervention and if dosages have impact on motor outcomes. Aims - To examine the effect on motor performance at 24-months corrected age following a parent-administered intervention performed with infants born preterm in the NICU. Intervention dosing and longitudinal motor performance were also analyzed. Study design - Single-blinded randomized multicenter clinical trial. Subjects - 153 infants born, gestational age ≤ 32 weeks at birth, were randomized into intervention or control group. Outcome measures - Infant Motor Performance Screening Test, Test of Infant Motor Performance, Peabody Developmental Motor Scales-2. Results - No significant difference was found between the intervention and the control group assessed with the PDMS-2 at 24-months CA. However, a significant positive association was found between dosing and the Gross Motor and Total Motor PDMS-2 scores. Analysis of longitudinal motor performance showed a decreasing motor performance between 6- and 24-months corrected age in both groups. Conclusions - There was no difference in motor performance between groups at 24-months corrected age. However, increased intervention dosage was positively associated with improved motor outcome

The World Social Situation: Development Challenges at the Outset of a New Century

World social development has arrived at a critical turning point. Economically advanced nations have made significant progress toward meeting the basic needs of their populations; however, the majority of developing countries have not. Problems of rapid population growth, failing economies, famine, environmental devastation, majority-minority group conflicts, increasing militarization, among others, are pushing many developing nations toward the brink of social chaos. This paper focuses on worldwide development trends for the 40-year period 1970-2009. Particular attention is given to the disparities in development that exist between the world’s “rich” and “poor” countries as well as the global forces that sustain these disparities. The paper also discusses more recent positive trends occurring within the world’s “socially least developed countries” (SLDCs), especially those located in Africa and Asia, in reducing poverty and in promoting improved quality of life for increasing numbers of their populations

General movement optimality score and general movements trajectories following early parent-administrated physiotherapy in the neonatal intensive care unit

Background The Prechtl General Movement Assessment (GMA) is a reliable tool for the functional assessment of the young nervous system. It is based on a global assessment of the quality of infants' movements. In addition, detailed steps of assessment have been developed – one for preterm and term age, and one for use between 3 and 5 months. One potential benefit of such a detailed analysis is the documentation of subtle changes in the infants' spontaneous movements caused by early intervention. Aim To present detailed scores of the infants' general movements (GMs) at preterm age, and of the infants' motor repertoire at 3 months' postterm age (PTA), for infants having participated in a randomized controlled trial (RCT) of early intervention, and to examine possible group differences. In addition, the aim is also to present the GMA from preterm to 3 months' PTA, comparing the intervention and the control group. Study design A retrospective study on infants who had participated in an RCT of parent-administered early intervention. Subjects 141 infants born very preterm. Outcome measures GMA, “Detailed Assessment of General Movements During Preterm and Term Age” and “Assessment of Motor Repertoire at 3 to 5 months”. Results The GMA and the detailed assessments of GMs conducted at 36 weeks' post menstrual age (PMA) showed the same distribution of normal and abnormal movements in both the intervention and in the control group, as did the assessment of motor repertoire at 3 months' PTA. Conclusion Neither the GMA nor the detailed assessments of GMs at 36 weeks' PMA and of the motor repertoire at 13 weeks' PTA suggest that early intervention, performed before term, changes the GMs of very preterm-born infants

Correlates of Normal and Abnormal General Movements in Infancy and Long-Term Neurodevelopment of Preterm Infants: Insights from Functional Connectivity Studies at Term Equivalence

Preterm infants born before 32 weeks gestation have increased risks for neurodevelopmental impairment at two years of age. How brain function differs between preterm infants with normal or impaired development is unknown. However, abnormal spontaneous motor behavior at 12–15 weeks post-term age is associated with neurodevelopmental impairment. We imaged brain blood oxygen level-dependent signals at term-equivalent age in 62 infants born at <32 weeks gestation and explored whether resting state functional connectivity (rsFC) differed with performances on the General Movement Assessment (GMA) at 12–15 weeks, and Bayley III scores at two years of corrected age. Infants with aberrant general movements exhibited decreased rsFC between the basal ganglia and regions in parietal and frontotemporal lobes. Infants with normal Bayley III cognitive scores exhibited increased rsFC between the basal ganglia and association cortices in parietal and occipital lobes compared with cognitively impaired children. Infants with normal motor scores exhibited increased rsFC between the basal ganglia and visual cortices, compared with children with motor impairment. Thus, the presence of abnormal general movements is associated with region-specific differences in rsFC at term. The association of abnormal long-term neurodevelopmental outcomes with decreased rsFC between basal ganglia and sub-score specific cortical regions may provide biomarkers of neurodevelopmental trajectory and outcome

Maternal alcohol and drug use during pregnancy affects the motor behaviour and general movements of infants aged 3–4 months

Background Exposure of alcohol and/or other addictive drugs in pregnancy is a documented risk factor for later neurological impairment. Aims The aim of the study was to determine whether infants suffering from prenatal exposure to addictive drugs and alcohol develop an abnormal motor behaviour at three to four months of age. Study design Controlled cohort study of infants exposed to alcohol and/or other addictive drugs in pregnancy who were recruited from a hospital follow-up programme. The control group consisted of healthy, unexposed infants. Subjects The study group of 108 infants exposed to alcohol and/or addictive drugs in pregnancy were enrolled based on referrals from primary health care. The control group included 106 infants who had not been exposed to the aforementioned substances. Outcome measures We assessed the general movements (Prechtl’s General-Movement-Assessment, GMA), the motor repertoire (Assessment-of-Motor-Repertoire, AMR), and the Alberta-Infant Motor-Scale (AIMS) in all infants at three to four months of age. Results None of the infants in either group had absent fidgety movements (FMs). In the study group 5(5%) had exaggerated FMs and 5(5%) had sporadic FMs; and 68(63%) infants in the study group displayed an abnormal movement character, compared to 23(22%) in the control group (p<0.001). On the AIMS, 46(44%) infants in the study group scored below the 10th percentile, compared to 2(3%) controls (p< 0.001). Conclusion The study describes an abnormal movement character of infants exposed to alcohol and/or addictive drugs in pregnancy when their motor repertoire was assessed at three to four months of age. The AIMS also showed negative effects on their motor behaviour

In-Motion-App for remote General Movement Assessment: a multi-site observational study

Objectives: To determine whether videos taken by parents of their infants' spontaneous movements were in accordance with required standards in the In-Motion-App, and whether the videos could be remotely scored by a trained General Movement Assessment (GMA) observer. Additionally, to assess the feasibility of using home-based video recordings for automated tracking of spontaneous movements, and to examine parents' perceptions and experiences of taking videos in their homes. Design: The study was a multi-centre prospective observational study. Setting: Parents/families of high-risk infants in tertiary care follow-up programmes in Norway, Denmark and Belgium. Methods: Parents/families were asked to video record their baby in accordance with the In-Motion standards which were based on published GMA criteria and criteria covering lighting and stability of smartphone. Videos were evaluated as GMA 'scorable' or 'non-scorable' based on predefined criteria. The accuracy of a 7-point body tracker software was compared with manually annotated body key points. Parents were surveyed about the In-Motion-App information and clarity. Participants: The sample comprised 86 parents/families of high-risk infants. Results: The 86 parent/families returned 130 videos, and 121 (96%) of them were in accordance with the requirements for GMA assessment. The 7-point body tracker software detected more than 80% of body key point positions correctly. Most families found the instructions for filming their baby easy to follow, and more than 90% reported that they did not become more worried about their child's development through using the instructions. Conclusions: This study reveals that a short instructional video enabled parents to video record their infant's spontaneous movements in compliance with the standards required for remote GMA. Further, an accurate automated body point software detecting infant body landmarks in smartphone videos will facilitate clinical and research use soon. Home-based video recordings could be performed without worrying parents about their child's development

Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study

Background: Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the Computer-based Infant Movement Assessment (CIMA) model, for clinically feasible early CP prediction based on infant video recordings. Methods: The CIMA model was designed to assess the proportion (%) of CP risk-related movements using a time–frequency decomposition of the movement trajectories of the infant’s body parts. The CIMA model was developed and tested on video recordings from a cohort of 377 high-risk infants at 9–15 weeks corrected age to predict CP status and motor function (ambulatory vs. non-ambulatory) at mean 3.7 years age. The performance of the model was compared with results of the general movement assessment (GMA) and neonatal imaging. Results: The CIMA model had sensitivity (92.7%) and specificity (81.6%), which was comparable to observational GMA or neonatal cerebral imaging for the prediction of CP. Infants later found to have non-ambulatory CP had significantly more CP risk-related movements (median: 92.8%, p = 0.02) compared with those with ambulatory CP (median: 72.7%). Conclusion: The CIMA model may be a clinically feasible alternative to observational GMA

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk

Importance Early identification of cerebral palsy (CP) is important for early intervention, yet expert-based assessments do not permit widespread use, and conventional machine learning alternatives lack validity. Objective To develop and assess the external validity of a novel deep learning–based method to predict CP based on videos of infants’ spontaneous movements at 9 to 18 weeks’ corrected age. Design, Setting, and Participants This prognostic study of a deep learning–based method to predict CP at a corrected age of 12 to 89 months involved 557 infants with a high risk of perinatal brain injury who were enrolled in previous studies conducted at 13 hospitals in Belgium, India, Norway, and the US between September 10, 2001, and October 25, 2018. Analysis was performed between February 11, 2020, and September 23, 2021. Included infants had available video recorded during the fidgety movement period from 9 to 18 weeks’ corrected age, available classifications of fidgety movements ascertained by the general movement assessment (GMA) tool, and available data on CP status at 12 months’ corrected age or older. A total of 418 infants (75.0%) were randomly assigned to the model development (training and internal validation) sample, and 139 (25.0%) were randomly assigned to the external validation sample (1 test set). Exposure Video recording of spontaneous movements. Main Outcomes and Measures The primary outcome was prediction of CP. Deep learning–based prediction of CP was performed automatically from a single video. Secondary outcomes included prediction of associated functional level and CP subtype. Sensitivity, specificity, positive and negative predictive values, and accuracy were assessed. Results Among 557 infants (310 [55.7%] male), the median (IQR) corrected age was 12 (11-13) weeks at assessment, and 84 infants (15.1%) were diagnosed with CP at a mean (SD) age of 3.4 (1.7) years. Data on race and ethnicity were not reported because previous studies (from which the infant samples were derived) used different study protocols with inconsistent collection of these data. On external validation, the deep learning–based CP prediction method had sensitivity of 71.4% (95% CI, 47.8%-88.7%), specificity of 94.1% (95% CI, 88.2%-97.6%), positive predictive value of 68.2% (95% CI, 45.1%-86.1%), and negative predictive value of 94.9% (95% CI, 89.2%-98.1%). In comparison, the GMA tool had sensitivity of 70.0% (95% CI, 45.7%-88.1%), specificity of 88.7% (95% CI, 81.5%-93.8%), positive predictive value of 51.9% (95% CI, 32.0%-71.3%), and negative predictive value of 94.4% (95% CI, 88.3%-97.9%). The deep learning method achieved higher accuracy than the conventional machine learning method (90.6% [95% CI, 84.5%-94.9%] vs 72.7% [95% CI, 64.5%-79.9%]; P < .001), but no significant improvement in accuracy was observed compared with the GMA tool (85.9%; 95% CI, 78.9%-91.3%; P = .11). The deep learning prediction model had higher sensitivity among infants with nonambulatory CP (100%; 95% CI, 63.1%-100%) vs ambulatory CP (58.3%; 95% CI, 27.7%-84.8%; P = .02) and spastic bilateral CP (92.3%; 95% CI, 64.0%-99.8%) vs spastic unilateral CP (42.9%; 95% CI, 9.9%-81.6%; P < .001). Conclusions and Relevance In this prognostic study, a deep learning–based method for predicting CP at 9 to 18 weeks’ corrected age had predictive accuracy on external validation, which suggests possible avenues for using deep learning–based software to provide objective early detection of CP in clinical settings