Postural alignment is impaired in adults with severe mobility limitations after an acquired brain injury: an observational study

Abstract

Background: Postural alignment impairments can be observed after acquired brain injury (ABI) and may be more prevalent in people with greater mobility impairment. However, investigations of postural alignment are limited by the lack of an evaluated method of assessment. This study examined postural alignment differences between healthy adults and adults with ABI, and evaluated repeatability of a new kinematic method for measuring postural alignment. Methods: Observational study of 14 adults with ABI who were non-ambulant at eight weeks post-injury, and 14 healthy adults. 3D kinematic data were recorded using motion capture technology in sitting and standing, on two separate days. A new Postural Alignment and Dispersion (PAD) score quantified average body segment displacements from the base of support (BoS) in the transverse plane. The two groups’ PAD scores were compared. Repeatability of the PAD score between days was evaluated using intraclass correlation coefficients (ICCs). Major findings: Greater postural alignment deviations were observed after ABI, including marked deficits in frontal and sagittal planes. ICCs ranged moderate (0.614) to excellent (0.945). Wide confidence intervals (CIs) for some ICCs indicated that repeatability of PAD scores could not be assured for all sitting and standing conditions. Conclusions: Postural alignment impairments occur in multiple planes after ABI and can be pronounced in people with severe mobility impairment. Evidence is needed to determine if addressing postural alignment impairments affects functional progress after ABI. PAD score measurement requires further evaluation to determine how repeatability can be optimised.

Keywords:

• Posture
• biomechanics
• trunk
• stroke
• head trauma

Acknowledgements

Thanks to the participants and to the South Australian Brain Injury Rehabilitation Service for their support.

Disclosure statement

The authors report no conflicts of interest.

Additional information

Funding

This work received support through an Australian Government Research Training Program Scholarship for the lead author, and grant funding from the Lifetime Support Authority of South Australia (grant GA00035) and Royal Adelaide Hospital Research Committee (grant 9705). DT is funded by the National Health & Medical Research Council as a Career Development Fellow (ID: 1126229). The funders played no role in the design, conduct, or reporting of this study.

Notes on contributors

Simon J. Mills

Simon J. Mills is Physiotherapy Principal Clinician at the South Australian Brain Injury Rehabilitation Service, working in inpatient and community rehabilitation. He completed his PhD at The University of Adelaide. His research interests include improving outcomes after very severe brain injury, methods for assessment, addressing biomechanical impairments and models of clinical education.

Michelle N. McDonnell

Michelle N. McDonnell is a Senior Neurological Physiotherapist who has extensive experience in all aspects of rehabilitation. Her current position involves provision of rehabilitation to community clients, but she has previously worked in research and academic positions. Her research track record includes publication of over 50 peer-reviewed publications, 24 as first author. She has received over $1million in research funding, and has supervised 2 PhD and 2 Masters candidates, and 5 Honours students to completion.

Shylie Mackintosh

Shylie Mackintosh is a Professor of Allied Health at the University of South Australia. She divides her time between teaching, administration and research. Her research expertise lies broadly with rehabilitation for people with neurological conditions, clinical biomechanics and the prevention of falls in older people. She is interested in both primary research investigating interventions as well as how to translate research evidence into practice.

Dominic Thewlis

Dominic Thewlis leads biomechanical research at The University of Adelaide. This extends to computational and numerical methods, including machine and deep learning, and to orthopaedic clinical decision making. Many clinical projects run at the Royal Adelaide Hospital, supporting clinically-informed research activity.