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There are very few studies in the literature examining the effect of age on motor imagery ability in children with cerebral palsy. To our knowledge, no studies have investigated the impact of other related factors. The aim of this study is to investigate the effects of age, sex, body mass index, Gross Motor Function Classification System (GMFCS) level, and Manual Ability Classification System (MACS) level on motor imagery abilities in children with cerebral palsy.
H1-1: Age has an effect on motor imagery abilities in children with cerebral palsy.
H1-2: Sex has an effect on motor imagery abilities in children with cerebral palsy.
H1-3: Clinical type has an effect on motor imagery abilities in children with cerebral palsy.
H1-4: Body mass index has an effect on motor imagery abilities in children with cerebral palsy.
H1-5: GMFCS level has an effect on motor imagery abilities in children with cerebral palsy.
H1-6: MACS level has an effect on motor imagery abilities in children with cerebral palsy.
Full description
Cerebral Palsy (CP) is a group of permanent movement and posture disorders resulting from non-progressive damage to the developing brain. Along with motor impairments, individuals with CP often experience sensory, cognitive, communication, behavioral, and musculoskeletal challenges. The most widely accepted classification, developed by the Surveillance of Cerebral Palsy in Europe (SCPE), categorizes CP into spastic, dyskinetic, and ataxic types. Spastic CP is the most common and may present with either bilateral or unilateral involvement. Dyskinetic CP includes dystonic and choreoathetotic subtypes.
Motor learning involves acquiring new skills to improve movement quality. While typically developing children learn motor activities naturally, children with CP may experience difficulties due to structural and motor planning deficits. Motor imagery (MI), defined as mentally simulating a movement without physical execution, is one strategy that supports motor learning. MI and actual movement engage similar brain regions and share cognitive and neurophysiological components.
MI can be experienced from a visual or kinesthetic perspective. Visual imagery involves watching the movement as an observer, while kinesthetic imagery involves internally experiencing the movement as the actor. Kinesthetic imagery is more closely associated with motor planning and functional improvement. MI can be explicit, involving conscious simulation of movement, or implicit, occurring unconsciously through internal body representations. Both forms are used in neurorehabilitation.
Previous studies on MI in children with CP have typically included participants aged 6 to 20 with relatively good motor and functional abilities. Although some researchers suggest that MI skills begin to develop between the ages of 5 and 7, findings are inconsistent, highlighting the need for further investigation.
Research exploring factors that may influence MI ability in children with CP is limited. While age has been studied to a small extent, the effects of sex, clinical type, body mass index (BMI), Gross Motor Function Classification System (GMFCS) level, and Manual Ability Classification System (MACS) level have not been thoroughly examined. This study aims to explore the influence of these variables on MI ability in children with CP. After recording the children's demographic characteristics (age, sex, height, weight, body mass index [BMI], and duration of physiotherapy), the following assessments will be conducted:
Gross Motor Function Classification System (GMFCS):
GMFCS is a five-level classification system used to assess gross motor function in individuals with CP, focusing on sitting, transfers, and mobility. It categorizes function based on self-initiated movement across specific age bands: 0-2, 2-4, 4-6, 6-12, and 12-18 years. Level I indicates independent walking with minimal limitations, while Level V indicates the need for a wheelchair for mobility.
Manual Ability Classification System (MACS):
MACS is used to classify how children with CP (aged 4-18 years) use their hands to handle objects in daily life. Level I indicates the highest manual ability, with ease in handling objects, while Level V indicates very limited ability and the need for maximum assistance.
Hand Laterality Task:
Implicit motor imagery ability will be assessed using the Hand Laterality Task, which requires participants to identify whether an image shows a left or right hand. The Recognise Hand app, developed by the NOI group, will be used for this purpose. Outcome measures include response accuracy (percentage of correct responses) and reaction time.
Mental Chronometry Task:
Explicit motor imagery will be assessed using the mental chronometry paradigm. Participants will first perform a physical movement and then imagine performing the same movement toward targets at varying distances. The duration of the actual and imagined movements will be recorded. The Box and Block Test will be used, and temporal congruence will be calculated using the delta time formula: [(actual movement time - imagined movement time) / ((actual movement time + imagined movement time) / 2)] x 100.
Box and Block Test:
This test evaluates manual dexterity and is commonly used in both clinical and research settings due to its simplicity and cost-effectiveness. Participants are instructed to move as many blocks as possible from one compartment to another in 60 seconds. The score reflects the number of successfully transferred blocks, with higher scores indicating better upper extremity performance. It will also be used in the mental chronometry task.
Movement Imagery Questionnaire for Children:
This questionnaire evaluates visual (internal, external) and kinesthetic imagery abilities. It includes 12 items: 4 for each imagery type. Participants will perform four movements physically and then imagine them from each perspective. Imagery vividness will be rated on a 7-point Likert scale (1 = very hard to feel, 7 = very easy to feel). Higher scores indicate better imagery ability.
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54 participants in 2 patient groups
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Seda AYAZ TAŞ, Phd
Data sourced from clinicaltrials.gov
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