App-Based Feedback for Rehabilitation Exercise Correction in Patients With Knee or Hip Osteoarthritis: Prospective Cohort Study

Abstract

Background The use of digital therapeutic solutions for rehabilitation of conditions such as osteoarthritis provides scalable access to rehabilitation. Few validated technological solutions exist to ensure supervision of users while they exercise at home. Motion Coach (Kaia Health GmbH) provides audiovisual feedback on exercise execution in real time on conventional smartphones. Objective We hypothesized that the interrater agreement between physiotherapists and Motion Coach would be noninferior to physiotherapists’ interrater agreement for exercise evaluations in a cohort with osteoarthritis. Methods Patients diagnosed with osteoarthritis of the knee or hip were recruited at a university hospital to perform a set of 6 exercises. Agreement between Motion Coach and 2 physiotherapists’ corrections for segments of the exercises were compared using Cohen κ and percent agreement. Results Participants (n=24) were enrolled and evaluated. There were no significant differences between interrater agreements (Motion Coach app vs physiotherapists: percent agreement 0.828; physiotherapist 1 vs physiotherapist 2: percent agreement 0.833; P<.001). Age (70 years or under, older than 70 years), gender (male, female), or BMI (30 kg/m2 or under, greater than 30 kg/m2) subgroup analysis revealed no detectable difference in interrater agreement. There was no detectable difference in levels of interrater agreement between Motion Coach vs physiotherapists and between physiotherapists in any of the 6 exercises. Conclusions The results demonstrated that Motion Coach is noninferior to physiotherapist evaluations. Interrater agreement did not differ between 2 physiotherapists or between physiotherapists and the Motion Coach app. This finding was valid for all investigated exercises and subgroups. These results confirm the ability of Motion Coach to detect user form during exercise and provide valid feedback to users with musculoskeletal disorders.

Use, and acceptability, of digital health technologies in musculoskeletal physical therapy: A survey of physical therapists and patients

“…These findings align with emerging research published in the field. It appears that wearable activity trackers, sensors, and mobile apps are the most frequently researched applications of digital health technologies for patients in musculoskeletal healthcare (Bailey et al., 2020; Biebl et al., 2021; Li et al., 2020; Machado et al., 2017; Ummels et al., 2020). For example, recent research into patient‐facing digital health technologies suggests that integration of digital health technologies into osteoarthritis management is scalable and allows patients to track, monitor, and progress their rehabilitation remotely (Biebl et al., 2021).…”

The Role of Artificial Intelligence in Future Rehabilitation Services: A Systematic Literature Review

“…The CNNs have been trained on large-scale datasets and on graphstructured data for motion analysis issue [35,77]. CNNs achieved outstanding accuracies in the computer vision field for human detection and pose estimation that is useful to compute position and orientation data of interesting joints [33,39,42,44], but also for activity monitoring [32,40,41,52] and movement evaluation [43]. Particularly CNN architectures such as ResNet [78] and AlexNet [79] achieved the highest accuracy respectively in pose estimation method for tracking human motion [42]and activity monitoring by processing some kinematic data [41].…”

“…Retrieved systems have been used to guide and assist motion while collecting objective data on movement quality and encouraging remote rehabilitation sessions via digital therapeutic approaches, providing access to quality therapy from a distance without needing constant supervision by a therapist on site. Different digital strategies have been individualized: exergame solutions that engage the patient to perform some functional movements providing relevant video and audio feedback during a serious game session [25,26,38,27,42,49]; digital coach solutions that, via speech [33,47,29], sometimes involving social humanoid robot to encourage activity participation [44], or app-based text notifications [46], offer automated conversational interaction to replace some human care tasks (reminders and motivational messages for medication, nutrition, and exercise, routine condition checks and health maintenance based on personal monitoring data).…”

“…Besides all these types of motion capture technologies, the recent developments in the computer vision field extended the use of low-cost RGB and depth sensors to perform motion analysis by the means of human pose estimation algorithms based on deep learning frameworks [33,30,42]. Human pose estimation is a field of computer vision that aims to predict the poses of human bodies by extracting joints from images and videos for motion analysis [65].…”

“…Human pose estimation is a field of computer vision that aims to predict the poses of human bodies by extracting joints from images and videos for motion analysis [65]. Contrarily to wearable sensors, AI-based human motion modeling enables commercial systems equipped with a camera and low-cost hardware, such as tablets and smartphones, to perform inexpensive and unobtrusive home-based monitoring in patients' daily life [33,35,36].…”