Poster The Joint Annual Meeting of the Stroke Society of Australasia (SSA) and Smartstrokes 2023

What baseline and intervention characteristics predict walking speed six months after stroke? (#179)

Neelam Nayak 1 , Sandra Brauer 1 , Suzanne Kuys 2 , Mohammad Ali Moni 1 , Niruthikha Mahendran 1
  1. School of Health and Rehabilitation Sciences, The University of Queensland, St Lucia, QLD, Australia
  2. School of Allied Health, Faculty of Health Sciences, Australian Catholic University, Banyo, QLD, Australia

BACKGROUND: High-intensity treadmill training and self-management strategies positively effect walking outcomes after stroke. However, it is unclear how these strategies can be matched to individuals after stroke.

AIMS: This study aimed to evaluate novel clusters of stroke survivors based on baseline and intervention characteristics, predicting improvement in walking speed six months after an intervention post-stroke.

METHODS: This study is a secondary analysis of data from a randomized controlled trial of adults within two months of stroke. Fifty-six participants received a self-management program embedded in high-intensity treadmill gait training (3 x 30-minute sessions per week, 8 weeks). Baseline characteristics included demographic details, daily step count, walking and exercise self-efficacy. Intervention characteristics included treadmill performance (speed, distance, and rate of perceived exertion) and self-management strategies used. Primary outcomes consisted of comfortable and fast walking speed, measured at baseline and six months after intervention. A machine learning-based unsupervised clustering approach was used to identify clusters. Multiple regression models were used to identify predictors.

RESULTS: Three distinct clusters were identified: Cluster 1 [n=20, mean age = 58 (11)], Cluster 2 [n=15, mean age = 70(9)] and Cluster 3 [(n=21, mean age = 61(11)].  Clusters had baseline mean comfortable walking speed of 1.2 (0.07) m/s, 0.9 (0.11) m/s and 0.64 (0.14) m/s respectively. Walking related self-efficacy and treadmill training speed predicted comfortable walking speed (adjusted r2 = 0.67, p<0.001). Self-management strategies and treadmill training distance predicted fast walking speed (adjusted r2 = 0.82, p<0.001).

CONCLUSIONS: This study highlights that adults with stroke may need different strategies to improve walking speed. Baseline characteristics such as walking speed, walking self-efficacy, fatigue and stroke severity, as well as intervention strategies including treadmill training speed, intensity and self-management could be used to target long-term walking speed changes in people with stroke.

ACKNOWLEDGEMENTS: This research was funded by National Health and Medical Research Council of Australia.