Senior Principal Research Fellow, NHMRC
Conjoint Professor, UNSW
+612 9399 1061
Professor Stephen Lord is a Senior Principal Research Fellow at Neuroscience Research Australia, Sydney, Australia. He has published over 400 papers in the areas of balance, gait and falls in older people and is acknowledged as a leading international researcher in his field. His research follows two main themes: the identification of physiological risk factors for falls and the development and evaluation of fall prevention strategies. Key aspects of this research have been the elucidation of sensorimotor factors that underpin balance and gait and the design and evaluation of exercise programs for older people including those at increased risk of falls, i.e. people with Parkinson’s disease, stroke, dementia and frailty. His methodology and approach to fall-risk assessment has been adopted by many researchers and clinicians across the world and he is actively engaged in initiatives aimed at implementing falls prevention evidence into policy and practice.
There is emerging evidence that visuo-spatial processing is involved in balance control during gait. Importantly, visuo-spatial processing may be key for fall avoidance as it enables one to precisely remember the position and physical characteristics of upcoming hazards; an essential skill for the safe navigation of everyday environments. Yet, investigations of visuospatial processing use for obstacle avoidance have been restricted to animal studies and young adults. No studies have been undertaken in older people or people with Parkinson’s Disease for whom visuo-spatial processing deficits are evident and associated with impaired postural control.
This series of studies will investigate visuo-spatial processing required for obstacle avoidance and navigation in older people, older people at high risk of falls and people with Parkinson’s Disease. We will use motion capture to investigate behavioural outcomes and a freely-worn brain imaging device, functional near-infrared spectroscopy to study cortical activation in regions of interest. We will conduct two experiments one involving an obstacle crossing task and another, a stepping task.
We hypothesize that older age, Parkinson’s Disease and increasing task complexity will result in increased risk of tripping and impaired visuo-motor performance, in the obstacle crossing task and in the stepping task, respectively.
This research will greatly improve our understanding of central mechanisms for fall risk and build on our recent behavioural work in this area.
Miss Angeliki Stivactas (Masters student UNSW), Dr Phu Hoang, Prof Stephen Lord, Dr Jasmine Menant
Gait dysfunction in Mulitple Sclerosis is an important risk factor for falls. Although there is detailed biomechanical evidence of impaired gait patterns in people with Multiple Sclerosis, there is a paucity of objective empirical data relating specific lower limb muscle strength deficits and gait impairments. Most studies to date have used manual muscle testing to investigate lower limb muscle strength and/or have only focused on knee flexors and extensors.
In this study, we aim to identify weak lower limb muscles contributing to gait impairment in Multiple Sclerosis.
Our experimental protocol involves a comprehensive assessment of isometric strength in eight major lower limb muscle groups using electronic strain gauges. We then conduct a full lower-limb gait analysis using motion capture and force platforms. We will conduct statistical analyses to determine which weak muscle groups are significantly associated with markers of gait impairment in Multiple Sclerosis (eg. knee range of motion during the gait cycle). We are also planning to use electromyography on the identified deficient muscle groups in a subset of participants.
Our research will identify the muscle groups contributing to poor gait, likely causing imbalance and trips in people with Multiple Sclerosis. This work is crucial for developing progressive resistance training programs that directly target weak muscle groups to improve gait in people with Multiple Sclerosis.
A randomised controlled trial to reduce the risk of falling in people with Parkinson’s disease.
An engaging self-managed neuro-rehabilitation program using eHealth technologies to improve mobility and enhance independence in people with Parkinson’s disease:
Parkinson’s disease is a multi-systems neurodegenerative disease with the severity of clinical symptoms (including postural instability, gait dysfunction an falls). With the population aging, the number of people affected by Parkinson’s disease is expected to double every 25 years presenting an increasing burden on health service and society as a whole. Falls are a common and devastating event in individuals with Parkinson’s disease and often precipitated by excessive gait variability, postural instability and freezing of gait.
Visual, attentional, haptic and auditory stimuli have been used to improve gait dysfunction in people with Parkinson’s disease. The aim of this project is to develop and evaluate a self-managed program using mHealth technology to improve mobility in people suffering from Parkinson’s disease. Dr Matthew Brodie was awarded a Michael & Elizabeth Gilbert Scholarship in Parkinson’s Disease Research.
An international alliance lays the groundwork for the widespread implementation of StandingTall.
This study targets a major need for older people for whom falls are a real risk that can have debilitating impacts on quality of life. It has been developed with major input from all partners and constitutes a valuable, collaborative partnership between researchers, experts in health promotion, health care providers and policy makers. Our partners for this project are the NSW Office of Preventive Health, Ministry of Health; the Clinical Excellence Commission; and the Agency for Clinical Innovation; two NSW Local Health Districts; i.e. Northern NSW and mid-North Coast; Austin Health, Uniting and the Northern Health Science Alliance in the United Kingdom.
The study aims to accelerate the implementation of StandingTall. It will address the final steps needed to scale up this innovative technology for widespread use by older people across Australia and England with prospects for further international translation. The overall aim of this international project is to establish integrated processes and pathways to deliver StandingTall to older people and to provide ongoing support as required. The project provides scope for further broad scale implementation and a model for incorporating StandingTall into existing health services and routine care.
Falls and functional decline are common in people with dementia. Falls are more likely to result in injury, death and institutionalisation when compared to older people without dementia. There is limited evidence that falls can be prevented in people with dementia. Strategies aimed at maintaining independence and preventing decline and falls are urgently needed. This research will a) further our understanding of fall risk and functional decline and b) explore novel fall and decline prevention programs, including the use of technology in older people with dementia.
Technological advances have enabled less expensive ways to quantify physical fall risk in the homes of older people.
We are exploring whether unobtrusive monitoring of activities of daily living or regular unsupervised directed routine assessments using new sensor-based technologies can predict falls in older adults more accurately.
We are developing and validating a range of mobile apps to assess fall risk factors in research settings and clinical practice; i.e. questionnaires (fear of falling, physical activity, etc), sensorimotor assessments (balance, vision, etc) and cognitive assessments (executive functioning, processing speed, etc.).
We are also working on Smart home IT support for frail elderly people who live alone.
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The aim of this study was to examine the effects of concern about falling on gait speed, adjusted for physiological fall risk and cognitive function. Gait speed, especially under dual-task conditions, was affected by concern about falling. Concern about falling was the strongest predictor of gait speed under all 4 conditions and should be included in routine geriatric assessments.