Exploring physiotherapist movement during patient and therapeutic handling in a laboratory setting.

Work-related musculoskeletal disorders (WRMSDs) are highly prevalent among physiotherapists, largely due to the physical demands of patient and therapeutic handling. Despite mandatory manual handling training and existing ergonomic guidance, WRMSD rates remain high, particularly in neurological rehabilitation, where hands on facilitation is frequent and complex. Current manual handling guidance is predominantly generalised across healthcare practitioners and lacks an evidence base that reflects physiotherapy biomechanics and the specific demands of physiotherapy practice. In particular, there is a lack of objective biomechanical data describing how physiotherapists move during patient handling tasks and how different handling positions influence musculoskeletal risk. The primary aim of this doctoral thesis was to test the feasibility and application of three-dimensional motion analysis in a laboratory setting to quantify physiotherapists' movement and associated musculoskeletal risk during neurological patient and therapeutic handling tasks. A secondary aim was to explore how physiotherapist positioning influences movement patterns and ergonomic risk, in order to contribute evidence to inform safer handling practices. A two-phase observational study design was employed. An initial Feasibility Study evaluated the technical feasibility, data quality, task suitability, and participant acceptability of capturing physiotherapists' handling movements using a Vicon optical motion capture system. This informed protocol refinements for a subsequent Pilot Study. The Pilot Study applied a full-body motion capture model combined with an inter-segmental spinal model to quantify spinal, shoulder, hip, and knee kinematics during two common neurological handling tasks: sit-to-stand and lower limb gait facilitation. Tasks were performed from multiple physiotherapist positions, including standing, sitting, and half-kneeling. Kinematic data were time-normalised and interpreted using established ergonomic risk assessment frameworks, including the Rapid Upper Limb Assessment (RULA) and Postural Load Index (PLI). Laboratory-based motion capture of complex physiotherapy handling tasks was feasible and acceptable following methodological refinement, although reliable kinetic data collection was not achievable without compromising movement authenticity. Pilot results demonstrated that physiotherapist position significantly influenced movement patterns and ergonomic risk. Across tasks, sustained thoracic flexion emerged as a consistent feature, frequently exceeding ergonomic thresholds. No single handling position minimised risk across all body regions; instead, different positions redistributed postural demand between the spine, upper limbs, and lower limbs. Inter-segmental spinal analysis revealed regional risks that were underestimated by ergonomic assessment tools that treat the trunk as a single segment, such as RULA, highlighting discrepancies between assessment methods. This research provides novel biomechanical evidence specific to physiotherapy practice, demonstrating that neurological patient handling involves sustained, multi-segment postural exposure that varies with physiotherapist position. The findings demonstrate that integrating inter-segmental biomechanical analysis with ergonomic assessment enables a more detailed characterisation of ergonomic risk during physiotherapy patient handling. The methodological framework developed provides a reproducible foundation for future research and supports the development of guidance, informed by task specific biomechanical evidence, for managing musculoskeletal risk in physiotherapy practice.

Author dc:creator

• Hetrick, Emma

Advisor dc:contributor.advisor

• A. Pavlova, L. Alexander, F. Douglas and K. Johnson

• Physiotherapy
• Work-related musculoskeletal disorders
• Patient handling
• Motion capture
• Ergonomics
• Biomechanics

Language dc:language

en

Identifier

oai:rgu-repository.worktribe.com:3463764
https://doi.org/10.48526/rgu-wt-3463764

OAI identifier oai:identifier

oai:rgu-repository.worktribe.com:3463764