Mobile technology and telemedicine for shoulder range of motion: validation of a motion-based machine-learning software development kit

Abstract

Mobile technology offers the prospect of delivering high-value care with increased patient access and reduced costs. Advances in mobile health (mHealth) and telemedicine have been inhibited by the lack of interconnectivity between devices and software and inability to process consumer sensor data. The objective of this study was to preliminarily validate a motion-based machine learning software development kit (SDK) for the shoulder compared with a goniometer for 4 arcs of motion: (1) abduction, (2) forward flexion, (3) internal rotation, and (4) external rotation. A mobile application for the SDK was developed and "taught" 4 arcs of shoulder motion. Ten subjects without shoulder pain or prior shoulder surgery performed the arcs of motion for 5 repetitions. Each motion was measured by the SDK and compared with a physician-measured manual goniometer measurement. Angular differences between SDK and goniometer measurements were compared with univariate and power analyses. The comparison between the SDK and goniometer measurement detected a mean difference of less than 5° for all arcs of motion (P > .05), with a 94% chance of detecting a large effect size from a priori power analysis. Mean differences for the arcs of motion were: abduction, -3.7° ± 3.2°; forward flexion, -4.9° ± 2.5°; internal rotation, -2.4° ± 3.7°; and external rotation -2.6° ± 3.4°. The SDK has the potential to remotely substitute for a shoulder range of motion examination within 5° of goniometer measurements. An open-source motion-based SDK that can learn complex movements, including clinical shoulder range of motion, from consumer sensors offers promise for the future of mHealth, particularly in telemonitoring before and after orthopedic surgery.