Search

To examine the applicability and process of change of Cognitive Functional Therapy (CFT) in the management of pain and disability in people with knee osteoarthritis who were offered knee replacement surgery and had risk factors for poor response to surgery. Single-case experimental design with a mixed-methods, repeated measures approach was used to investigate the process of change through CFT in four participants. Qualitative interviews investigated beliefs, behaviours and coping responses, and self-reported measures assessed pain, disability, psychological factors, and function at 25 timepoints. Study registered with the Australian and New Zealand Clinical Trials Registry (ACTRN12619001491156). Qualitative data indicate that CFT promoted helpful changes in all participants, with two responses observed. One reflected a clear shift to a biopsychosocial conceptualisation of osteoarthritis, behavioural re-engagement and the view that a knee replacement was no longer necessary. The other response reflected a mixed conceptualisation with dissonant beliefs about osteoarthritis and its management. Psychological and social factors were identified as potential treatment barriers. Overall, quantitative measures supported the qualitative findings. The process of change varies between and within individuals over time. Psychological and social barriers to treatment have implications for future intervention studies for the management of knee osteoarthritis. Cognitive Functional Therapy is applicable in the management of knee osteoarthritis. Reconceptualisation of osteoarthritis reflected a helpful change. Psychological and social factors emerged as barriers to recovery. [ABSTRACT FROM AUTHOR]

How people talk about osteoarthritis may impact outcomes, including uptake of guideline recommendations related to activity-based lifestyles and interventions. In this editorial, we describe 2 key ways of talking, based on findings from our systematic review of 62 qualitative studies exploring the perceptions of people with knee osteoarthritis (n = 1208), their carers (n = 28), and clinicians (n = 2403). Among raw quotes extracted from the studies, we observed a dominant impairment-based way of talking and a participatory based way of talking. These ways of talking form a novel framework to help clinicians understand what people think and do about osteoarthritis. [ABSTRACT FROM AUTHOR]

SYNOPSIS: This editorial series raises awareness among clinicians about how ways of talking about orthopaedic conditions can influence what people who are seeking health care (1) think about their health and (2) what they do to manage their health. In part 1, we introduce you to ways of talking about health, using osteoarthritis as a case study. In part 2, we describe 2 contrasting ways of talking about osteoarthritis and how changing the way you share information and ideas with people seeking care may impact clinical decisions. In part 3, we offer strategies to help you shift the way you communicate with people with osteoarthritis to promote uptake of best practice recommendations and support healthy, active lifestyles. [ABSTRACT FROM AUTHOR]

Clinicians lack objective means for monitoring if their knee osteoarthritis patients are improving outside of the clinic (e.g., at home). Previous human activity recognition (HAR) models using wearable sensor data have only used data from healthy people and such models are typically imprecise for people who have medical conditions affecting movement. HAR models designed for people with knee osteoarthritis have classified rehabilitation exercises but not the clinically relevant activities of transitioning from a chair, negotiating stairs and walking, which are commonly monitored for improvement during therapy for this condition. Therefore, it is unknown if a HAR model trained on data from people who have knee osteoarthritis can be accurate in classifying these three clinically relevant activities. Therefore, we collected inertial measurement unit (IMU) data from 18 participants with knee osteoarthritis and trained convolutional neural network models to identify chair, stairs and walking activities, and phases. The model accuracy was 85% at the first level of classification (activity), 89–97% at the second (direction of movement) and 60–67% at the third level (phase). This study is the first proof-of-concept that an accurate HAR system can be developed using IMU data from people with knee osteoarthritis to classify activities and phases of activities. [ABSTRACT FROM AUTHOR]