Research Advances: January 2014

The VA has a comprehensive research agenda to help the newest generation of Veterans -- those returning from operations Enduring Freedom, Iraqi Freedom, and New Dawn. In addition to exploring new treatments for traumatic brain injury and other complex blast-related injuries, VA researchers are examining ways to improve the delivery of health care services for these Veterans and promote their reintegration back into their families, communities, and workplaces.This publication reviews recent advances in research about Veterans' health and well-being

Non-contact measures to monitor hand movement of people with rheumatoid arthritis using a monocular RGB camera

Hand movements play an essential role in a person’s ability to interact with the environment. In hand biomechanics, the range of joint motion is a crucial metric to quantify changes due to degenerative pathologies, such as rheumatoid arthritis (RA). RA is a chronic condition where the immune system mistakenly attacks the joints, particularly those in the hands. Optoelectronic motion capture systems are gold-standard tools to quantify changes but are challenging to adopt outside laboratory settings. Deep learning executed on standard video data can capture RA participants in their natural environments, potentially supporting objectivity in remote consultation. The three main research aims in this thesis were 1) to assess the extent to which current deep learning architectures, which have been validated for quantifying motion of other body segments, can be applied to hand kinematics using monocular RGB cameras, 2) to localise where in videos the hand motions of interest are to be found, 3) to assess the validity of 1) and 2) to determine disease status in RA. First, hand kinematics for twelve healthy participants, captured with OpenPose were benchmarked against those captured using an optoelectronic system, showing acceptable instrument errors below 10°. Then, a gesture classifier was tested to segment video recordings of twenty-two healthy participants, achieving an accuracy of 93.5%. Finally, OpenPose and the classifier were applied to videos of RA participants performing hand exercises to determine disease status. The inferred disease activity exhibited agreement with the in-person ground truth in nine out of ten instances, outperforming virtual consultations, which agreed only six times out of ten. These results demonstrate that this approach is more effective than estimated disease activity performed by human experts during video consultations. The end goal sets the foundation for a tool that RA participants can use to observe their disease activity from their home.Open Acces

Arthritis and disability

Executive summary: Arthritis Australia commissioned the Social Policy Research Centre (SPRC) at UNSW Australia to carry out research on the lived experience of people with arthritis related conditions. This report outlines the methods, findings and implications of the research. Arthritis is the second leading cause of disability and the most common cause of chronic pain in Australia; it is the most prevalent long-term health condition, affecting 3 million people or about 15 per cent of the population. Studies are available on the health costs and loss of productivity associated with arthritis, but not as much is understood about the extent to which arthritis is associated with disability–who is affected, how people are affected, what helps people cope with their condition day to day, and how support services can be improved. Improving understanding of the disability impact of arthritis is particularly important given the transition in Australia to the National Disability Insurance Scheme and the impact this may have on service availability and delivery

HeartHealth: new adventures in serious gaming

We present a novel, low-cost, interactive, exercise-based rehabilitation system. Our research involves the investigation and development of patient-centric, sensor-based rehabilitation games and surrounding technologies. HeartHealth is designed to provide a safe, personalised and fun exercise environment that could be deployed in any exercise based rehabilitation program. HeartHealth utilises a cloud-based patient information management system built on FIWARE Generic Enablers,and motion tracking coupled with our sophisticated motion comparison algorithms. Users can record customised exercises through a doctors interface and then play the rehabilitation game where they must perform a sequence of their exercises in order to complete the game scenario. Their exercises are monitored, recorded and compared by our Motion Evaluation software and real-time feedback is than given based on the users performance

Home-based physical therapy with an interactive computer vision system

In this paper, we present ExerciseCheck. ExerciseCheck is an interactive computer vision system that is sufficiently modular to work with different sources of human pose estimates, i.e., estimates from deep or traditional models that interpret RGB or RGB-D camera input. In a pilot study, we first compare the pose estimates produced by four deep models based on RGB input with those of the MS Kinect based on RGB-D data. The results indicate a performance gap that required us to choose the MS Kinect when we tested ExerciseCheck with Parkinson’s disease patients in their homes. ExerciseCheck is capable of customizing exercises, capturing exercise information, evaluating patient performance, providing therapeutic feedback to the patient and the therapist, checking the progress of the user over the course of the physical therapy, and supporting the patient throughout this period. We conclude that ExerciseCheck is a user-friendly computer vision application that can assist patients by providing motivation and guidance to ensure correct execution of the required exercises. Our results also suggest that while there has been considerable progress in the field of pose estimation using deep learning, current deep learning models are not fully ready to replace RGB-D sensors, especially when the exercises involved are complex, and the patient population being accounted for has to be carefully tracked for its “active range of motion.”Published versio

Economic evaluation of care for the chronically ill: A literature review

Financial problems of governments and the consequent urge to set limits on health care growth have increased the importance of economic rationalization. A systematic review of the present body of knowledge might facilitate the need to set priorities in health care policies and research in an ageing society with growing numbers of the elderly and chronically ill. After explaining the purpose and methods of full economic evaluation, we review the literature on 3 major chronic diseases, diabetes mellitus (20 publications), rheumatoid arthritis (15) and chronic obstructive pulmonary disease (COPD) and asthma (8). This review serves 2 objectives: to review the existing literature and to assess its quality. The review reveals a lack of full economic evaluation in this sector of health care. The total number of references to the specified chronic diseases covers 5% of all economic literature and 44% of all references under Index Medicus' heading 'economics', while the burden of illness is substantial, resulting in high indirect costs to the patients themselves and to society. The dominant approach is cost-effectiveness analysis (71%), followed by cost-benefit analysis (20%). Cost-utility analysis is rare (9%), partly because it is still in the phase of development. However, this approach can deal better with the objectives of many interventions in chronic care, i.e. increasing the quality rather than the quantity of life. We make a plea for full economic evaluation of chronic care programmes and for the development of quality of life measures which cover the broad domain of well-being of the chronically ill

Understanding Falls Risk Screening Practices and Potential for Electronic Health Record Data-Driven Falls Risk Identification in Select West Virginia Primary Care Centers

Unintentional falls among older adults are a complex public health problem both nationally and in West Virginia. Nationally, nearly 40% of community-dwelling adults age 65 and older fall at least once a year, making unintentional falls the leading cause of both fatal and non-fatal injuries among this age group. This problem is especially relevant to West Virginia, which has a population ageing faster on average than the rest of the nation. Identifying falls risk in the primary care setting poses a serious challenge. Currently, the Timed Get-Up-and-Go test is the only recommended screening tool for determining risk. However, nationally this test is completed only 30-37% of the time. Use of electronic health record data as clinical decision support in identifying at-risk patients may help alleviate this problem. However, to date there have been no published studies on using electronic health record data as clinical decision support in the identification of this particular population. This presents opportunity to contribute to the fields of falls prevention and health informatics through novel use of electronic health record data. That stated, this research is designed to: 1) develop an understanding of current falls risk screening practices, facilitators, and barriers to screening in select West Virginia primary care centers; 2) assess the capture of falls risk data and the quality of those data to help facilitate identification of at-risk patients; and 3) build an internally validated model for using electronic health record data for identification of at-risk patients. Through focus group discussions with primary care partners, we find a significant lack of readiness to innovatively use routinely collected data for population health management for falls prevention. The topic of falls risk identification is a rarely discussed topic across these sites, with accompanying low rates of screening and ad-hoc documentation. The need for enhanced team-based care, policy, and procedure surrounding falls is evident. Using de-identified electronic health record data from a sample of West Virginia primary care centers, we find that it is both feasible and worthwhile to repurpose routinely collected data to identify older adult patients at-risk for falls. Among 3,933 patients 65 and older, only 133 patients (3.4%) have an indication in their medical records of falling. Searching the free text data was vital to finding even this low number of patients, as 33.8% were identified using free text searches. Given the focus group findings, underreporting of falls on the part of the patients and missed opportunities to learn of falls due to lack of information sharing across health care service sites are also contributing factors. Similarly, documentation of falls risk assessments were sparse with only 23 patients (0.6%) having documentation of a falls risk assessment in their medical records at some point in the past. As with falls, locating documentation of falls risk assessments was largely dependent on semi-structured and free text data. Current Procedural Terminology coding alone missed 26.1% of all falls risk assessments. Repurposing electronic health record data in a population health framework allows for concurrent examination of primary and secondary falls risk factors in a way which is sensitive to time constraints of the routine office visit, complementary to the movement toward Meaningful Use, while providing opportunity to bolster low screening rates

Outlook Magazine, Summer 2018

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