Towards a Clinical Decision-Making Algorithm Guiding Locomotor Therapy Modality in Subacute Stroke: An Exploratory Study

Nicole Prideaux; Christopher Barr; Claire Drummond; Maayken van den Berg

doi:10.1016/j.jstrokecerebrovasdis.2021.106112

Towards a Clinical Decision-Making Algorithm Guiding Locomotor Therapy Modality in Subacute Stroke: An Exploratory Study

J Stroke Cerebrovasc Dis. 2021 Dec;30(12):106112. doi: 10.1016/j.jstrokecerebrovasdis.2021.106112. Epub 2021 Sep 30.

Authors

Nicole Prideaux¹, Christopher Barr², Claire Drummond³, Maayken van den Berg⁴

Affiliations

¹ Bachelor of Applied Science Physiotherapy (honours), Master Clinical Rehabilitation, Physiotherapist, Rehabilitation, Aged Care, and Palliative Division, Southern Adelaide Local Health Network: c/o Flinders Medical Centre, Flinders Drive, Bedford Park, South Australia, 5042, Australia. Electronic address: nicole.prideaux@sa.gov.au.
² Bachelor Physiology, Sports Science and Nutrition (honours), Masters Research Bioengineering, PhD Bioengineering, College of Nursing and Health Sciences, Flinders University: GPO Box 2100, Adelaide 5001, South Australia, Australia. Electronic address: chris.barr@flinders.edu.au.
³ Bachelor of Applied Science (Exercise and Sports Science), Human Movement (honours), PhD Health Sciences, College of Nursing and Health Sciences, Flinders University; GPO Box 2100, Adelaide 5001, South Australia, Australia. Electronic address: claire.drummond@flinders.edu.au.
⁴ Bachelor of Physiotherapy, Master of Movement Sciences, PhD Rehabilitation Sciences, College of Nursing and Health Sciences, Flinders University, GPO Box 2100, Adelaide 5001, South Australia, Australia. Electronic address: maayken.vandenberg@flinders.edu.au.

PMID: 34601241
DOI: 10.1016/j.jstrokecerebrovasdis.2021.106112

Abstract

Objectives: To propose a clinical decision-making algorithm guiding modality choice and transition from the Lokomat® robotic to body-weight supported treadmill training in subacute stroke, due to current evidence being limited, making clinical decisions difficult.

Materials and methods: For 10 adult patients with subacute stroke completing Lokomat® therapy, physiotherapist clinical judgement regarding body-weight supported treadmill training readiness and the following objective measurements were collected; Functional Ambulation Category; sit to stand/standing ability; Lokomat® settings; maximal active hip and knee flexion in standing; and gait biomechanics during body-weight supported treadmill training. Based on observed patterns a proposed clinical decision-making algorithm was developed.

Results: Clinical judgement deemed four of 10 participants ready to transition to body-weight supported treadmill training. Unlike participants judged not ready, these participants had: a) a Functional Ambulation Category of 1; b) independence with sit to stand and standing with even weight bearing; c) Lokomat®: Body-Weight Support <30%, Guidance Force <30-35%, speed >2.0kph; d) >45° standing active hip and knee flexion; e) no significant issues with physiological stepping in treadmill training or only requiring assistance from one therapist to achieve this.

Conclusion: Participants judged ready for transition from the Lokomat® to body-weight supported treadmill training presented with increased independent functional ability, more challenging Lokomat® settings, greater active volitional lower-limb control, and less issues with physiological stepping in treadmill training, than those participants judged not ready. Results were translated into a proposed clinical decision-making algorithm guiding transition from the Lokomat® to body-weight supported treadmill training, to be further tested in clinical trials.

Keywords: Algorithm; Body-weight support treadmill; Clinical decision-making; Lokomat®; Robotics; Stroke; Subacute; Transition.

MeSH terms

Adult
Algorithms*
Clinical Decision-Making*
Exercise Therapy* / methods
Humans
Stroke* / therapy