An automated, electronic assessment tool can accurately classify older adult postural stability
Introduction
In 2012, older adults in the US were treated for an estimated 1.76 million falls in the emergency room (Burns et al., 2016). Falls are the leading cause of fatal injury, can increase hospital stays and readmissions, and are an important factor affecting morbidity, mortality, and general independence among older adults (Perell et al., 2001). The cost of falls in older adults in the US is high. In 2015, the direct medical costs of fatal falls for people aged 65 years and over totalled $637.5 million (USD), and $31.3 billion (USD) was spent on injuries following non-fatal falls (Burns et al., 2016). Given the risk and cost of falls and fall-related injuries, the assessment of balance and identification of fall risk should be a critical part of routine clinical care for older adults (Xu et al., 2018). Indeed, risk assessment and stratification initiatives may be able to decrease the incidence of falls in older adults, but often require clinical visits that are costly and logistically difficult for both clinicians and patients.
The Berg Balance Scale (BBS) is a commonly used measure of balance within the clinical setting (Berg et al., 1995). However, the BBS has demonstrated floor and ceiling effects in the past (Blum and Korner-Bitensky, 2008), and has limited application in non-ambulatory individuals given most components of the test require the individual to be standing. We previously proposed the modified BBS (MBBS) as an assessment of seated balance for non-ambulatory people, which may enable balance to be safely assessed without the need for supervision (Dehbandi et al., 2017). The BBS is also a time-consuming assessment (15–20 min), whereas the MBBS can be completed in 5–6 min, which may be particularly advantageous given clinical consults are time limited.
Computerised posturography is a more sensitive measure but typically requires expensive equipment (e.g. force platforms) and trained staff to administer the test and interpret the data collected. Furthermore, the set-up, calibration and data collection are often time-consuming, which might limit its clinical applicability. There are several low-cost, automated, electronic devices that are widely available and have the capacity to measure gait and balance, but until recently, their capacity to sensitively and reliably quantify human motor behaviour has not been established. A comprehensive automated kinematic analysis might offer an innovative solution to the current risk landscape that is the measurement and interpretation of balance in the clinical setting.
The Microsoft Kinect 2 (MK2, Microsoft Corporation, Redmond, WA, USA) is a combined high-definition (HD) video camera and active infrared (IR) camera with a depth sensor designed for 3-dimensional (3D) body tracking. It accurately records the movements of the head, trunk and limbs in 3D space by tracking the displacement of 25 inferred anatomical landmarks; or “centroids”. This technology has widespread application in assessing balance and gait control. It has been shown to accurately assess static (Clark et al., 2015) and dynamic (Eltoukhy et al., 2017) balance, and gait (Mentiplay et al., 2015), including multiple components of the timed-up-and-go (Vernon et al., 2015).
When collected under appropriate conditions, data from the MK2 can be paired with machine learning algorithms to sensitively identify clinically relevant information. Our previous work demonstrated that data collected using the MK2 can be used to discriminate between three distinct states of postural stability in 12 healthy adults performing a modified version of the MBBS (Dehbandi et al., 2017). Taken together, previous work with the MK2 suggests that it could offer a novel approach to identifying individuals with impaired balance. In addition, its low-cost, accessibility and customizability make it, and similar technologies, of relevance to the telemedicine and rehabilitation communities.
The aim of this study was to evaluate the capability of the MK2 to predict the BBS score of older adults with varying levels of postural stability from a few simple, seated movements performed in front of the camera. The research makes progress towards a brief, accurate measure of balance that could be applied both within and away from the clinical setting. This could alleviate time-pressures placed on clinicians/therapists and enhance the robustness of the clinical assessment of balance, and thereby reduce the risk of falling.
Section snippets
Participants
Participants were recruited from the Burke Rehabilitation Hospital and were included in the study if they were over the age of 18 years, were able to safely sit in an upright position unsupported, could understand and follow verbal commands, and were able to provide informed consent. The participants involved in the study were recruited from a pool of older adults from the Burke Rehabilitation Hospital. This group of participants included stroke survivors (<6 months post-stroke), older adults
Results
A total of 74 participants (35 female) were recruited for this study. Participants were primarily older adults with an average age of 73.1 years (46–95). Participants included 59 individuals with chronic disease (i.e. cardiovascular disease, musculoskeletal injury, neurological conditions), of which 23 were experiencing multi-morbidity. In addition to this, 15 otherwise healthy older adults were recruited into the study. No adverse events were reported.
In order to perform our intended analysis,
Discussion
In this study, the MK2, a low-cost depth sensing video camera, was paired with sophisticated modelling to develop a method of stratifying fall risk in older adults. Our model was highly accurate in predicting participants as either high fall risk (i.e. <40) or moderate-low fall risk (i.e. ≥40), with only one of 43 participants misclassified in our test dataset. Moreover, the median error of our model’s predictions was less than one point on the BBS. This protocol may therefore hold substantial
Conclusion
We have shown that we can capture and interpret kinematic data to generate accurate predictions of balance scores in older adults with and without balance impairments. This methodology has the potential to support clinicians and therapists in their clinical decision-making when assessing the balance and potential fall-risk of their patients. It is also highly relevant to telerehabilitation service providers as we now have the potential to accurately measure balance, and the risk of falling, in
Acknowledgements
We wish to acknowledge Dr John Long, Mr Silverio Bumanlag, Mr Victor He and Ms Anna Lampe for their contribution to the study. We would also like to acknowledge the study participants who gave up their time to participate in this study.
Conflict of interest
The authors have no conflicts.
Sponsor's role
No sponsors were involved in this study.
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