Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions

Abstract

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

Keywords: continuous blood glucose; data collection; diabetes management; longitudinal monitoring; machine learning; wearable devices.

Figure 1

The three wearable devices used in this study.

Figure 2

Screenshots of the developed App to interface with the armband wearable sensor and to register activities. (a) Main interface of the App to connect with the sensor. (b) Activity logging interface. (c) The general instructions and information interface.

Figure 3

A snippet of the logging sheet.

Figure 4

An overview of the task distribution during the 4-day trial.

Figure 5

A snippet of the daily diary used by the participants to record their hourly activities.

Figure 6

Corneal confocal microscopy screening test. (a) The CCM device. (b) Demonstration of the test. (c) Central corneal sub-basal nerve plexus. (d) Inferior whorl.

Figure 7

The Sudoscan device used to perform the sudomotor test. (a) The device. (b) Demonstration of the test.

Figure 8

The placement of the 3 ECG electrodes on the chest highlighted with circles.

Figure 9

Data availability for the CGM and three wearables acquired from two participants demonstrating the discrepancy in the amount of data collected from each participant. (a) Worst case. (b) Best case. Note: the points on the time axis represent missing data.