In May of this year, UWC Physiotherapy will be represented by five staff members at the World Confederation of Physical Therapy Congress in Geneva, Switzerland. Here is the work that they will present (in alphabetical order).
Factors influencing participation among persons with stroke living in South Africa. (Joseph C, Conradsson D, Rhoda A).
Background: Participation, i.e. the involvement in life situations, including age and culture appropriate roles and activities, is of one the most important goals after stroke. The complexity of stroke impairments, coupled with the influence of contextual factors, almost always result in participation restrictions. Purpose: To determine factors, as derived from the domains of the ICF, that influence participation among community-dwelling persons living with stroke in South Africa. Methods: Forty-nine conveniently selected persons with chronic stroke were included in this cross-sectional study evaluating functioning outcomes. The main dependent outcome, participation, was measured using the Subjective Index of Physical and Social Outcome (SIPSO) scale. This ordinal scale ranges from 0-40 with higher scores indicating greater participation. Independent factors related to socio-demographic and environmental variables, as well as stroke severity, balance performance, self-efficacy, activities (as measured with the Barthel Index and gait velocity) and physical activity volume. Multiple regression analysis was carried out to determine independently associated factors influencing participation. Results: Of the 49 initially included, 42 presented with completed data. The mean age of this cohort was 58 years, with a mean time since stroke of 30 months. The mean (SD) SIPSO participation score was 24.92 (9.97). Independent factors related to the total score of SIPSO were age (β=0.14; p=0.041), balance performance (β=0.38; p=0.006), and falls self-efficacy (β= -0.44; p<0.001). This model explains 76% of the variance in the total SIPSO score. Conclusions: The data suggest that participation is influenced by diverse factors, including age, balance impairments and self-efficacy. Implications: Apart from age, the remaining factors could be addressed via comprehensive rehabilitation programs. These programs should address underlying impairments and incorporate self-management principles.
Mortality rate and risk indicators for
Background: A spinal cord injury (SCI) results in a change, either temporary or permanent, in the cord’s normal motor, sensory or autonomic function. These changes predispose people who survive the initial injury to premature death. Previous studies have indicated that the mortality rate among the spinal cord community remains higher when compared to the general population, however, data pertaining to South Africa are lacking. Purpose: To determine the mortality rate and risk indicators of mortality at four years post-traumatic spinal cord injury in the City of Cape Town, South Africa. Methods: A prospective, regional population-based study design was used. The study population consisted of all participants with TSCI who were enrolled in an earlier incidence study that was conducted in 2013/2014. For the follow-up study, an inclusive sampling strategy was used. Descriptive statistics were used to describe the cohort and to present the mortality rate. Inferential statistics, i.e. bivariate logistic regression analysis was used to identify factors associated with mortality. Results: A total number of 55 persons (63%) were alive and completed the full survey, 21 persons (24%) had demised by the 4 years post-injury inquiry, and 11 people (13%) were classified as alive but not reachable/ “missing” during the data collection period. : Of those forming part of the study, the mortality rate was 24% four years post injury. The main cause of death reported was death due to septicaemia (n=7; 33%) followed by unknown cause of death (n=7; 33%), death due to pressure ulcers (n=5; 24%), and the least common cause of death reported was death classified as other with persons (n=2; 9.5%) reported other causes of death (stomach cancer, natural cause Persons with an incomplete injury were four times less likely to die (OR: 0.2; 95% confidence interval (CI): 0.07-0.58) and those having transport-related injuries were at almost nine times less risk of death in comparison to falls (OR: 0.11; 95%CI: 0.01-0.76). Conclusion: The study revealed that almost one-quarter of persons with TSCI have died four years post injury. The significant risk indicators for mortality were the completeness of injury and aetiology. A better understanding of why those with complete injuries and those having their SCI due to falls is required in order to develop prevention strategies.
Implications: The results of the study could be used to appropriately allocate resources in healthcare, for people with TSCI, in South Africa in order to improve their chances of survival. Improving the longevity of people with TSCI will enable physiotherapists to play a critical role in maintaining and improving functioning that is necessary for living a full and productive life.
Background: Traumatic spinal cord injury (TSCI) is one of the most devastating injuries resulting in varying degrees of disability. A high incidence rate of traumatic spinal cord injuries resulting mainly from violence- related has been reported in South Africa. Following a TSCI, the ability to participate in meaningful life roles and activities in and outside the home can change and diminish significantly. Successful community reintegration following a TSCI is considered an important goal of rehabilitation as this has been positively associated with quality of life,
An introduction to machine learning in healthcare: Implications for clinicians.
Background: Machine learning algorithms are enhancing clinical decision-making through the statistical analysis of very large data sets that are too complex for human beings to interpret on their own. Important applications of machine learning (ML) in healthcare include clinical decision support, diagnosis and prediction, patient monitoring and coaching, surgical assistance, patient care, and systems management. As the digital information we interact with in healthcare is increasingly filtered, shaped and analysed by algorithms we see that there are clinical and ethical implications for clinicians and patients. Purpose: There is increasing evidence that ML and AI will see significant vertical and horizontal integration across the health sector, with profound impacts on practice and training. This presentation aims to provide an introduction to the topic of machine learning and artificial intelligence in the context of healthcare and physiotherapy practice. Methods: A review of the literature was conducted between 2015-2018 in order to identify the ways in which ML algorithms are being used across the health sector. Because this is a relatively new area of research, the search was not limited to physiotherapy but included all health professions, and also made use of a wide variety of keywords that are often conflated in the mainstream media e.g. AI, artificial intelligence, computer vision, expert systems, machine/deep learning, neural networks, robotics. While these keywords represent separate fields of AI research they are all driven primarily by advances in the sub-domain of machine learning. Results: Disruptions in clinical practice as a result of AI-based diagnosis, prediction and reasoning will require that healthcare professionals reevaluate their fitness for purpose in an intelligence age that is characterised by altered relationships between therapists, patients, data, and algorithms. Human connection will be the key to success while at the same time we must use advanced technology – including AI-based systems and machine learning – to enhance our capacity to care for each other, to learn effectively over the course of our lives, and to develop creative solutions for the problems that matter to us. Conclusion(s): Machine learning algorithms are already “smarter” than us within certain narrow domains of clinical practice and will increasingly take over some of the cognitive and physical tasks that were previously the sole domain of human beings. Successful clinical practice in the 21st century will require that we understand how to analyse and interpret the decisions of ML algorithms and for this it is essential that clinicians are involved in the development, implementation and evaluation of these systems in clinical practice. Implications: Unless health professionals are actively engaged in a conversation around ML and artificial intelligence in clinical practice, we run the risk that our clinical decision-making will be subject to machine intelligence, rather than being informed by it. The challenge we face at the beginning of the 21st century is to bring together computers and humans in ways that enhance human well-being, augment human ability and expand human capacity.
Health promotion for non-communicable diseases: perceptions of physiotherapy and general practitioners in the Southern Province of Zambia.
We’re really excited to have such a large group presenting their work on such a big stage and we’ll have an updated report available when we get back after the conference. Once again, congratulations to everyone involved.