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Evaluation of a blended learning approach on stratified care for physiotherapy bachelor students
(2023)
Background: Stratified models of care are valuable for addressing psychosocial factors which influence the outcome of patients with musculoskeletal disorders. Introducing such models in undergraduate training has the potential to propagate this knowledge with evidence and foster its implementation. The objective of this paper is to explore the perception and changes in the fear-avoidance beliefs of physiotherapy students participating in a developed blended learning course on stratified care.
Methodology: A mixed-methods with a convenient sample of two consecutive cohorts were given a blended learning course on stratified care for patients with low back pain. The blended learning course comprised scientific rudiments and application of stratified care in clinical practice conceptualised using the KERN’ 6-step approach. The exam scores, perceptions, performance on self-reflection-tests and pre- and post-scores on The Tampa Scale for Kinesiophobia for Physiotherapists’ (TSK-PT) were obtained. After gaining clinical experience, participants were invited to discuss their clinical experiences and perceptions in workshops. The quantitative data was analysed explorative-descriptively. The qualitative data was analysed following an inductive coding system with constant comparisons.
Results: Ninety-one participants consented to the evaluation (mean age = 22.9 ± 1.6 years), 66% were female. Exam scores correlated with time spent in training (r = 0.30) and scores on self-reflection-tests 1 and 2 (r = 0.40 and r = 0.41). Participants in both cohorts described the learning resources as promoting their interest in the subject (72% and 94%), up-to-date (91% and 93%) and helpful (91% and 97%). The fear-avoidance scores for participants decreased from 53.5 (± 9.96) to 40.1 (± 12.4) with a large effect size (d = 1.18). The regression model [F (2, 49) = 1151.2, p < 0.001] suggests that pre-TSK-PT and the interest of participants in the training predicted post-TSK-PT. The workshop participants (n = 62) all worked in clinical practice. Emerging from the analysis were 4 categories (evolving to maturity in practice, perceiving determinants of stratified care, strategising for implementation and adopting an outlook for future practice).
Conclusion: The quality of engagement in learning, training strategy and interest in the subject contributes immensely to learning outcomes. This blended learning course was successful in reducing kinesiophobia and influencing the participants’ attitude towards care with the potential of being translated into long-term practice.
Deep brain stimulation (DBS) is an established therapy for movement disorders such as in Parkinson's disease (PD) and essential tremor (ET). Adjusting the stimulation parameters, however, is a labour-intensive process and often requires several patient visits. Physicians prefer objective tools to improve (or maintain) the performance in DBS. Wearable motion sensors (WMS) are able to detect some manifestations of pathological signs, such as tremor in PD. However, the interpretation of sensor data is often highly technical and methods to visualise tremor data of patients undergoing DBS in a clinical setting are lacking. This work aims to visualise the dynamics of tremor responses to DBS parameter changes with WMS while patients performing clinical hand movements. To this end, we attended DBS programming sessions of two patients with the aim to visualise certain aspects of the clinical examination. PD tremor and ET were effectively quantified by acceleration amplitude and frequency. Tremor dynamics were analysed and visualised based on setpoints, movement transitions and stability aspects. These methods have not yet been employed and examples demonstrate how tremor dynamics can be visualised with simple analysis techniques. We therefore provide a base for future research work on visualisation tools in order to assist clinicians who frequently encounter patients for DBS therapy. This could lead to benefits in terms of enhanced evaluation of treatment efficacy in the future.