МОЖЛИВОСТІ ТЕЛЕРЕАБІЛІТАЦІЇ У ВІДНОВЛЕННІ РУХОВИХ ФУНКЦІЙ У ДІТЕЙ З НЕВРОЛОГІЧНОЮ ПАТОЛОГІЄЮ
Bibliographic record
Abstract
Neurodevelopmental disorders, particularly cerebral palsy and other impairments of the central nervous system (CNS), are frequently associated with persistent motor deficits that significantly affect children's ability to perform daily activities and participate fully in age-appropriate social, educational, and physical environments. Early childhood is a critical period for motor development, and timely rehabilitation interventions during this window of heightened neuroplasticity can have a profound impact on long-term functional outcomes. Traditional rehabilitation approaches often require frequent in-person visits to specialized centers, which may not be feasible for many families due to geographical remoteness, lack of transportation, economic constraints, or limited availability of trained professionals. In this context, telerehabilitation has emerged as a promising alternative or adjunct to conventional care. It leverages modern information and communication technologies to deliver therapeutic services remotely, thereby overcoming many logistical and systemic barriers to care. Telerehabilitation can ensure continuous access to therapy, facilitate individualized programming, and foster stronger involvement of parents and caregivers in the rehabilitation process, which is a key factor in the success of pediatric interventions. The aim of this paper is to analyze contemporary literature on the effectiveness of telerehabilitation in promoting motor function recovery in children with neurological disorders. This narrative review synthesizes evidence from randomized controlled trials (RCTs), systematic reviews, scoping reviews, and cohort studies that assess the impact of various remote interventions on motor skills, functional independence, participation in daily life, and overall quality of life. The paper also discusses commonly used outcome measurement tools, such as the Gross Motor Function Measure (GMFM), the Pediatric Evaluation of Disability Inventory (PEDI), and the Canadian Occupational Performance Measure (COPM), which are instrumental in quantifying progress and tailoring interventions. Furthermore, the International Classification of Functioning, Disability and Health (ICF) framework is explored as a valuable model for designing and evaluating telerehabilitation programs. Findings indicate that telerehabilitation can significantly enhance motor development, reduce the severity of functional limitations, improve treatment adherence, and empower families to take an active role in the therapeutic journey. Successful programs often combine structured, play-based home activities with real-time virtual support from professionals, gamified platforms to enhance motivation, mobile applications for tracking progress, and virtual or augmented reality technologies to simulate engaging therapeutic environments. Nonetheless, challenges remain, including the need for standardized protocols, sufficient training for providers, data privacy and security concerns, and ensuring equitable access to digital health tools across diverse populations. Further high-quality studies are needed to establish best practices and to fully integrate telerehabilitation into pediatric neurorehabilitation pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".