Quantification of head and trunk control for children
Research type
Research Study
Full title
Quantification of head and trunk control for children with neuromotor and neuromuscular disorders
IRAS ID
233469
Contact name
Ian Loram
Contact email
Clinicaltrials.gov Identifier
N/A, N/A
Duration of Study in the UK
2 years, 11 months, 31 days
Research summary
Head and trunk control (trunk control) is mandatory for effective performance of everyday functional activities such as use of vision, sitting or any upper limb activity. Children with cerebral palsy (CP), spinal muscular atrophy (SMA) and inherited neuromuscular disorders (NMD) experience impairments in trunk control which are a fundamental component of their condition.
Children with CP show a direct relationship between poor trunk control and compromised function. Infants with SMA1 have similar difficulty acquiring trunk control as do children with severe CP. The increasing muscle weakness in children with NMD leads to deterioration in trunk control with subsequent scoliosis. Accurate diagnosis of trunk control can enable individualised physiotherapy, when combined with treatment as usual, to improve general motor function. An objective tool is needed to provide evidence encompassing multiple therapies and centres; it will also help to monitor effectiveness of therapeutic drugs; and enable detecting in trunk control changes, pre-scoliosis, to provide appropriate treatment at the optimal time.
Our hypothesis is that segmental trunk control can be measured objectively and efficiently, classified to seven segmental levels. Our project will i) develop live imaging analysis technology to automate the assessment of trunk control in sitting, ii) provide a cost-effective and accessible tool usable within any clinic without increasing assessment time and iii) publish an online interactive database disseminating a public standard, a reference and a training resource for measuring trunk control, increasing understanding and enhancing expertise. Assessments of trunk control of children in sitting (400 CP, 20 SMA1, 40 TD, 40 NMD) will be recorded using 3D cameras available for the home market. Machine learning methods will be used to train and validate the automated diagnosis. The tool will be validated against expert clinical assessment and deployed in a format suitable for use by clinical staff in a routine clinical environment.REC name
London - Brent Research Ethics Committee
REC reference
19/LO/1981
Date of REC Opinion
3 Jan 2020
REC opinion
Further Information Favourable Opinion