Maximizing The Value of Wearable By The Remote Collection And Analysis Of Raw 100hz Data

Abstract

This study was designed to look for patterns in raw accelerometer data potentially associated with a specific type of motor movement, namely scratching, to show that raw and aggregated data can be transmitted remotely outside of the traditional clinical setting. This research demonstrates that data from accelerometers has value beyond the traditional sleep and activity endpoints and could be used in remote studies. Two healthy volunteers (A and B) were provided with accelerometers and hubs. The hubs were SIM enabled to allow for continuous data collection. Volunteer A wore the device for 24 hours and used a diary to identify 27 scratching events of approximately 30 seconds duration. Volunteer B wore the device for 8 hours and used a diary to identify 7 scratching events. Raw 100 Hz accelerometer data was transmitted remotely via the hubs to the centralized study center, from where it was further processed and analyzed. An analytical model was developed using the data from Volunteer A to identify scratching events at a 10 second epoch level. This algorithm achieved sensitivity and specificity values of 99 and 100%, respectively for Volunteer A. The algorithm was further evaluated on unseen (from the model’s point of view) Volunteer B and achieved sensitivity and specificity values of 99 and 86%, respectively. Accelerometer-based wearables are gaining acceptance in clinical trials as a means of generating objective endpoints for sleep and activity using validated algorithms. This study has shown that the application of suitable algorithms to raw accelerometer data has the potential to generate clinically relevant outcome measures associated with patient motor movement patterns, which can have significance in studies looking at tremor and itch and other clinical symptoms. The ability to generate and transmit raw data from a patient’s home facilitates the integration of this methodology into remote and virtual trials.