Policy brief: adaptive cycling equipment for individuals with neurodevelopmental disabilities as durable medical equipment

- Durable medical equipment (DME) policies require that the equipment be medically necessary; however, adaptive cycling equipment (bicycles and tricycles) are usually not deemed medically necessary.- Individuals with neurodevelopmental disabilities (NDD) are at high risk for secondary conditions, both physical and mental, that can be mitigated by increasing physical activity.- Significant financial costs are associated with the management of secondary conditions.- Adaptive cycling can provide improved physical health of individuals with NDD potentially reducing costs of comorbidities.- Expanding DME policies to include adaptive cycling equipment for qualifying individuals with NDD can increase access to equipment.- Regulations to ensure eligibility, proper fitting, prescription, and training can optimize health and wellbeing.- Programs for recycling or repurposing of equipment are warranted to optimize resources

Systematic review of physical activity and exercise interventions to improve health, fitness and well-being of children and young people who use wheelchairs.

AIM: To perform a systematic review establishing the current evidence base for physical activity and exercise interventions that promote health, fitness and well-being, rather than specific functional improvements, for children who use wheelchairs. DESIGN: A systematic review using a mixed methods design. DATA SOURCES: A wide range of databases, including Web of Science, PubMed, BMJ Best Practice, NHS EED, CINAHL, AMED, NICAN, PsychINFO, were searched for quantitative, qualitative and health economics evidence. ELIGIBILITY: participants: children/young people aged >25 years who use a wheelchair, or parents and therapists/carers. Intervention: home-based or community-based physical activity to improve health, fitness and well-being. RESULTS: Thirty quantitative studies that measured indicators of health, fitness and well-being and one qualitative study were included. Studies were very heterogeneous preventing a meta-analysis, and the risk of bias was generally high. Most studies focused on children with cerebral palsy and used an outcome measure of walking or standing, indicating that they were generally designed for children with already good motor function and mobility. Improvements in health, fitness and well-being were found across the range of outcome types. There were no reports of negative changes. No economics evidence was found. CONCLUSIONS: It was found that children who use wheelchairs can participate in physical activity interventions safely. The paucity of robust studies evaluating interventions to improve health and fitness is concerning. This hinders adequate policymaking and guidance for practitioners, and requires urgent attention. However, the evidence that does exist suggests that children who use wheelchairs are able to experience the positive benefits associated with appropriately designed exercise. TRIAL REGISTRATION NUMBER: CRD42013003939

Decision trees for detection of activity intensity in youth with cerebral palsy

PURPOSE - To develop and test decision tree (DT) models to classify physical activity (PA) intensity from accelerometer output and Gross Motor Function Classification System (GMFCS) classification level in ambulatory youth with cerebral palsy (CP); and 2) compare the classification accuracy of the new DT models to that achieved by previously published cut-points for youth with CP. METHODS - Youth with CP (GMFCS Levels I - III) (N=51) completed seven activity trials with increasing PA intensity while wearing a portable metabolic system and ActiGraph GT3X accelerometers. DT models were used to identify vertical axis (VA) and vector magnitude (VM) count thresholds corresponding to sedentary (SED) (/=1.5 and /=3 METs). Models were trained and cross-validated using the 'rpart' and 'caret' packages within R. RESULTS - For the VA (VA_DT) and VM decision trees (VM_DT), a single threshold differentiated LPA from SED, while the threshold for differentiating MVPA from LPA decreased as the level of impairment increased. The average cross-validation accuracy for the VC_DT was 81.1%, 76.7%, and 82.9% for GMFCS levels I, II, and III, respectively. The corresponding cross-validation accuracy for the VM_DT was 80.5%, 75.6%, and 84.2%, respectively. Within each GMFCS level, the decision tree models achieved better PA intensity recognition than previously published cut-points. The accuracy differential was greatest among GMFCS level III participants, in whom the previously published cut-points misclassified 40% of the MVPA activity trials. CONCLUSION - GMFCS-specific cut-points provide more accurate assessments of MVPA levels in youth with CP across the full spectrum of ambulatory ability

Measuring reliability and validity of the ActiGraph GT3X accelerometer for children with cerebral palsy: A feasibility study

PURPOSE The purposes of this study were to: 1) establish inter-instrument reliability between left and right hip accelerometer placement; 2) examine procedural reliability of a walking protocol used to measure physical activity (PA), and; 3) confirm concurrent validity of accelerometers in measuring PA intensity as compared to the gold standard of oxygen consumption measured by indirect calorimetry. METHODS Eight children (mean age: 11.9; SD: 3.2, 75% male) with CP (GMFCS levels I-III) wore ActiGraph GT3X accelerometers on each hip and the Cosmed K4b^{2} portable indirect calorimeter during two measurement sessions in which they performed the six minute walk test (6MWT) at three self-selected speeds (comfortable/slow, brisk, fast). Oxygen consumption (VO2) and accelerometer step and activity count data were recorded. RESULTS Inter-instrument reliability of ActiGraph GT3X accelerometers placed on left and right hips was excellent (ICC=0.96-0.99, CI_{95}: 0.81-0.99). Reproducibility of the protocol was good/excellent (ICC=0.75-0.95, CI_{95}: 0.75-0.98). Concurrent validity of accelerometer count data and VO2 was fair/good (rho=0.67, p< 0.001). The correlation between step count and VO2 was not significant (rho=0.29, p=0.2). CONCLUSION This preliminary research suggests that ActiGraph GT3X accelerometers are reliable and valid devices to monitor PA during walking in children with CP and may be appropriate in rehabilitation research and clinical practice. ActiGraph GTX3 step counts were not valid for this sample and further research is warranted

Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy

Abstract Background Cerebral palsy (CP) is the most common physical disability among children (2.5 to 3.6 cases per 1000 live births). Inadequate physical activity (PA) is a major problem effecting the health and well-being of children with CP. Practical, yet accurate measures of PA are needed to evaluate the effectiveness of surgical and therapy-based interventions to increase PA. Accelerometer-based motion sensors have become the standard for objectively measuring PA in children and adolescents; however, current methods for estimating physical activity intensity in children with CP are associated with significant error and may dramatically underestimate HPA in children with more severe mobility limitations. Machine learning (ML) models that first classify the PA type and then predict PA intensity or energy expenditure using activity specific regression equations may be more accurate than standalone regression models. However, the feasibility and validity of ML methods has not been explored in youth with CP. Therefore, the purpose of this study was to develop and test ML models for the automatic identification of PA type in ambulant children with CP. Methods Twenty two children and adolescents (mean age: 12.8 ± 2.9 y) with CP classified at GMFCS Levels I to III completed 7 activity trials while wearing an ActiGraph GT3X+ accelerometer on the hip and wrist. Trials were categorised as sedentary (SED), standing utilitarian movements (SUM), comfortable walking (CW), and brisk walking (BW). Random forest (RF), support vector machine (SVM), and binary decision tree (BDT) classifiers were trained with features extracted from the vector magnitude (VM) of the raw acceleration signal using 10 s non-overlapping windows. Performance was evaluated using leave-one-subject out cross validation. Results SVM (82.0–89.0%) and RF (82.6–88.8%) provided significantly better classification accuracy than BDT (76.1–86.2%). Hip (82.7–85.5%) and wrist (76.1–82.6%) classifiers exhibited comparable prediction accuracy, while the combined hip and wrist (86.2–89.0%) classifiers achieved the best overall performance. For all classifiers, recognition accuracy was excellent for SED (94.1–97.9%), good to excellent for SUM (74.0–96.6%) and brisk walking (71.5–86.0%), and modest for comfortable walking (47.6–70.4%). When comfortable and brisk walking were combined into a single walking class, recognition accuracy ranged from 90.3 to 96.5%. Conclusions ML methods provided acceptable classification accuracy for detection of a range of activities commonly performed by ambulatory children with CP. The resultant models can help clinicians more effectively monitor bouts of brisk walking in the community. The results indicate that 2-step models that first classify PA type and then predict energy expenditure using activity specific regression equations are worthy of exploration in this patient group