Ignite Creation Date:
2024-05-06 @ 7:41 PM
Last Modification Date:
2024-10-26 @ 3:11 PM
Study NCT ID:
NCT06090383
Status:
RECRUITING
Last Update Posted:
2024-07-05
First Post:
2023-10-09
Brief Title:
Feasibility and Discriminant Validity of Monitoring Movement Behavior of Adolescents With Cerebral Palsy
Sponsor:
Rigshospitalet Denmark
Organization:
Rigshospitalet Denmark
Study Overview
Official Title:
Wearable and Deep Learning-Based Recognition of Real-World Movement Behavior of Adolescents With Cerebral Palsy Feasibility and Discriminant Validity Study
Status:
RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
A new artificial intelligence network has been developed to monitor real-world daytime and nighttime movement behavior of adolescents with cerebral palsy CP The network uses seven wearable sensors to recognize lying sitting and standing as well as walking and movements of both arms and legs This information can be useful for healthcare professionals to understand and influence change in movement behavior leading to benefits for the health of adolescents with cerebral palsy This study aims to examine the acceptability and technical dependability of monitoring the movement behavior of adolescents with cerebral palsy for 72 hours using wearable sensors Additionally the study aims to evaluate the networks ability to discriminate between control and individuals with CP different subgroups of individuals with CP as well as the incidence of sleep disturbance in the entire cohort
Detailed Description:
Cerebral palsy CP is a non-progressive disorder resulting from injuries or abnormalities in fetal or early infant brain development According to registries from European countries the condition affects 2-3 out of every 1000 live births An individual with CP typically presents with motor development disorders that cause abnormal patterns of movement and posture due to impaired coordination of movements and muscle tone regulation People with cerebral palsy can also have various other problems including sensory and cognitive problems and sleep disturbances These symptoms result in limitations in activity level and societal participation throughout the individuals life Adolescents and even children as young as seven may experience a decline in motor ability leading to changes in their movement behavior Healthcare professionals rely on various observations and measurements performed in clinical and hospital settings to assess and treat individuals with CP However there is some uncertainty about whether these assessments truly reflect real-life movement behaviors as using an impaired extremity in everyday life frequently deviates from its motor capacity There is an absence of robust tools that capture daytime and nighttime movement behavior in real-world settings rather than in clinical or controlled environments Hemiparesis is the most common marker of CP making asymmetrical deficits a target for intensive interventions such as physical and occupational therapy Yet no clinical tools are available that document asymmetrical differences in the real world in children and adolescents with CP An objective method to measure real-world movement patterns would allow therapists to identify individuals who need a more comprehensive evaluation and to target interventions and other management strategies more precisely This would help children and adolescents with CP gain motor skills to maximize independence Further objectively observing individuals with CP in their daily lives is essential to gain insights into functional decline It has been observed that children and adolescents with CP are more likely to experience sleep-related difficulties such as difficulty initiating sleep frequent nocturnal awakenings discomfort while in bed and early morning awakenings As sleep quality plays a vital role in health-related quality of life it is crucial to have objective methods to evaluate and monitor potential sleep problems in a real-world context
A deep-learning convolutional neural network has been modeled to recognize postures lying sitting and standing the activity of walking and movements of the right and left extremities The network uses accelerometer and gyroscope data from 7 wearable sensors Testing of the networks performance found that it surpasses human annotators in accurately classifying the movement behavior of healthy and typically developed adults These findings are currently under review and have yet to be published The present protocol details the methodology for assessing the feasibility of real-world movement behavior monitoring and the discriminant validity of the network in adolescents with CP and controls
The feasibility evaluation examines the technology used eg potential data loss and the credibility of data output as well as user acceptance eg sensor wear time and adverse events The networks discriminant ability will be assessed by the networks ability to differentiate between controls and CP severity eg scores on the Gross Motor Functional Classification Scale - Expanded and revised GMFCS-ER different types of CP differently affected body parts of the participating adolescents with CP as well as individuals who have and have not sleep problems in the entire cohort
A deep-learning convolutional neural network has been modeled to recognize postures lying sitting and standing the activity of walking and movements of the right and left extremities The network uses accelerometer and gyroscope data from 7 wearable sensors Testing of the networks performance found that it surpasses human annotators in accurately classifying the movement behavior of healthy and typically developed adults These findings are currently under review and have yet to be published The present protocol details the methodology for assessing the feasibility of real-world movement behavior monitoring and the discriminant validity of the network in adolescents with CP and controls
The feasibility evaluation examines the technology used eg potential data loss and the credibility of data output as well as user acceptance eg sensor wear time and adverse events The networks discriminant ability will be assessed by the networks ability to differentiate between controls and CP severity eg scores on the Gross Motor Functional Classification Scale - Expanded and revised GMFCS-ER different types of CP differently affected body parts of the participating adolescents with CP as well as individuals who have and have not sleep problems in the entire cohort
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None