Wearable Technology in Shoulder Range of Motion

Normal Upper Extremity

telemonitoring

The scope of the research is to determine if a motion-based software development kit may substitute for a manual goniometer. If so, this opens up the possibility of remote telemonitoring of upper extremities patients.

The Capture mobile application or app (FocusMotion, Santa Monica, California) was custom designed and securely installed on a single (CCF) smartphone used by the Lead Co-Investigator, Dr. Ramkumar, and serves as a private user interface for the proprietary software development kit (SDK). The Capture app uses a Bluetooth connection to pair with the Pebble Time Round smartwatch to allow for remote timekeeping of the smartphone while in use by the participant. The smartwatch was loaned by FocusMotion to the investigators for the purpose of the study. This proprietary SDK (FocusMotion) processes user motions recorded by the hardware (accelerometer, gyroscope, magnometer). As data is collected, the software development kit (SDK) classifies the motion as repetitive or non-repetitive, and repetitive motions are further analyzed. As the user completes a workout or movement, the SDK tracks the number of repetitions performed, technique, and rest time. The SDK can provide real-time feedback to the user regarding their form by comparing the movement to a template of an ideal motion for that particular action. The SDK is compatible with any operating system, phone, or wearable device, providing seamless integration with pre-existing technologies. Measurements from the SDK will be compared to those taken with a manual goniometer for four shoulder maneuvers (abduction, forward flexion, internal rotation, external rotation). Each of the 10 subjects will perform 5 trials of each maneuver for consistency and reproducibility. Participants without shoulder pain, with full shoulder range of motion and without prior shoulder surgery will be recruited. No other exclusion criteria will be instituted. Individuals will be recruited from CCF undergraduate and graduate medical education programs. Statistical analysis will be employed to assess the shoulder motion arcs measured from both the SDK and the manual goniometer. Univariate analyses (chi-square for categorical and t-test for continuous variables) will be performed to compare the angle measurements from each patient and angle measurement. Power analysis indicated a 94% chance of detecting a large effect size and a 60% chance of detecting a medium effect size between the two groups at the 5% confidence level. For 1 sample t-tests, power analysis indicated that there was a 92% chance of detecting a medium effect size significant at the 5% confidence level. All data analysis will be performed using Microsoft. Excel analytics software version 14.5.4 A p-value cutoff of \<0.05 was used to determine statistical significance. All adverse events will be reported to the Institutional Review Board.

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