ArticleMedicine and science in sports and exercise2016
Decision Trees for Detection of Activity Intensity in Youth with Cerebral Palsy.
Article in Medicine and science in sports and exercise, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- Psychometric Properties of Physical Activity Questionnaires for Children and Adolescents: An Updated Systematic Review.Sports medicine (Auckland, N.Z.) · 2025Pooled it
- Intervenção de esportes modificados para melhorar metas de participação e competências de atividade em crianças deambuladoras com paralisia cerebral: um ensaio clínico randomizado.Developmental medicine and child neurology · 2026Trial
- Modified sports intervention for improving participation goals and activity competence in ambulant children with cerebral palsy: A randomized controlled trial.Developmental medicine and child neurology · 2026Trial
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- Exploring the Relationship Between Physical Activity and ICF Domains in Young Adults with Cerebral Palsy: A Comparison of Unilateral and Bilateral Cases.Journal of clinical medicine · 2026Article
- Validity of PROMISBehavioral sciences (Basel, Switzerland) · 2025Article
- Physical Activity in Children and Young Adults With Cerebral Palsy: Results From a Three-Month Exercise Intervention.European journal of sport science · 2025Article
- Monitoring of child-specific activities in ambulatory children with and without developmental disabilities.BMC pediatrics · 2025Article
- Psychometrics of Wearable Devices Measuring Physical Activity in Ambulant Children With Gait Abnormalities: A Systematic Review and Meta-analysis.Archives of rehabilitation research and clinical translation · 2024Review
- Emerging methods for measuring physical activity using accelerometry in children and adolescents with neuromotor disorders: a narrative review.Journal of neuroengineering and rehabilitation · 2024Review
- Validation of two novel human activity recognition models for typically developing children and children with Cerebral Palsy.PloS one · 2024Article
- Inverse distance weighting to rapidly generate large simulation datasets.Journal of biomechanics · 2023Article
- Using unsupervised machine learning to quantify physical activity from accelerometry in a diverse and rapidly changing population.PLOS digital health · 2023Article
- Article
- An evaluation of data processing when using the ActiGraph GT3X accelerometer in non-ambulant children and adolescents with cerebral palsy.Clinical physiology and functional imaging · 2023Article
- Accelerometer-measured physical activity, sedentary behavior, and sleep in children with cerebral palsy and their adherence to the 24-hour activity guidelines.Developmental medicine and child neurology · 2023Article
- Article
- Validation and Determination of Physical Activity Intensity GT3X+ Cut-Points in Children and Adolescents with Physical Disabilities: Preliminary Results in a Cerebral Palsy Population.Children (Basel, Switzerland) · 2023Article
- Is Physical Activity-Related Self-Efficacy Associated with Moderate to Vigorous Physical Activity and Sedentary Behaviour among Ambulatory Children with Cerebral Palsy?Physiotherapy Canada. Physiotherapie Canada · 2022Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
Abstract
purposeTo 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 compare the classification accuracy of the new DT models to that achieved by previously published cut points for youth with CP.
methodsYouth 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 METs), light-intensity PA (LPA) (≥1.5 and <3 METs) and moderate-to-vigorous PA (MVPA) (≥3 METs). Models were trained and cross-validated using the "rpart" and "caret" packages within R.
resultsFor the VA (VA_DT) and VM DT (VM_DT), a single threshold differentiated LPA from SED, whereas the threshold for differentiating MVPA from LPA decreased as the level of impairment increased. The average cross-validation accuracies for the VC_DT were 81.1%, 76.7%, and 82.9% for GMFCS levels I, II, and III. The corresponding cross-validation accuracies for the VM_DT were 80.5%, 75.6%, and 84.2%. Within each GMFCS level, the DT 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.
conclusionsThe GMFCS-specific cut points provide more accurate assessments of MVPA levels in youth with CP across the full spectrum of ambulatory ability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.