How Smartwatches Could Help People with Type 1 Diabetes 

Posted on by kerry.turcsi

JGI Seed Corn Funding Project Blog 2023/24: Miranda Armstrong

Introduction

Type 1 diabetes (T1D) requires consistent self-management, which places a large burden on those who live with it. We explored the role smartwatches could play in reducing that burden. 

Image contains photos of a continuous glucose monitor, smartwatch, closed loop algorithm and Insulin pump
Figure 1: Theoretical closed-loop system that uses smartwatch data in its algorithm. Closed-loop systems without smartwatch integration are the current state of the art of T1D technology. They begin to automate the T1D management process by using data from the eco-system of devices to predict future changes in blood glucose and change insulin dosage to counteract these changes.

Aims

The project aimed to collect and build a dataset that would allow for exploration into the potential of smartwatches in T1D management. This would include both data from the smartwatch and T1D technology the participants used, and user experience of using the smartwatch alongside their typical T1D management. To meet the aim, the following goals were set: 

  1. Collect data from participants, including from smartwatches and T1D devices, and in interviews and focus groups. 
  1. Clean, anonymise, and combine data from different sensors into a consistent format, and transcribe the interviews and focus groups. 
  1. Hold an online data challenge using a sample of the collected data to promote the dataset and highlight potential uses for it. 
  1. Release the dataset publicly to allow other researchers to use it as part of their work and therefore increase the value of the dataset. 

What was achieved

Two graphs. Top graph is Blood glucose, Insulin and Carbohydrates against Time. The bottom graph is Heart rate and steps against time
Figure 2: An example day of data from one participant, with some of the data available. The upper axis highlights the data available to current commercial closed-loop systems and the lower axis shows some smartwatch data from the same period. 

Data Collection

The project recruited 24 participants, and each were given a smartwatch or could use their own. Over six months, participants donated data from their smartwatches and type 1 diabetes (T1D) devices to create a dataset aimed at exploring the integration of smartwatch data into a closed-loop algorithm. This dataset reflects real-life conditions and participants used a range of T1D technology. Over 2000 days, the data that was donated had a high coverage from all the devices the participant used. During this time participants were involved in interviews and focus groups to discuss their opinions of the smartwatches and potential roles they could see in T1D management. A total of 62 interviews and 11 focus groups were completed across the study period. 

Data Processing

We processed a large amount of data to prepare it for public use. The smartwatch and T1D data were cleaned and anonymised (so no one involved in the study could be identified) and then organised into two formats. One was an easy-to-use dataset for researchers to test their algorithms, and the other kept the data in its original form for deeper exploration. We also transcribed and anonymised the interviews and focus groups so other researchers could analyse them to understand the participants’ experience of using the smartwatch. 

Initial Findings

Initial engagement with the interviews and focus group data has highlighted several potential uses for smartwatches in T1D management. These include as a device to display data quickly and discretely to the user, as an interface with T1D technology for easier access, and as a data source to inform management decision making around activity. There are also design implications highlighted in this analysis. These include utilising automation to provide benefit without increasing user burden, allowing customisation to accommodate the wide range of user preferences and usage patterns to promote uptake, and flexibility to allow these systems to adapt to changing user needs and ensure use of the device into the future. 

Future Plans

The data challenge and the public release of the dataset are scheduled for later this year.  We plan to run the competition from mid-September to the end of November 2024, with £1600 in prize vouchers available across entries. If you would like to hear more details about the competition, please leave your details in this form. The whole dataset will then be published after the competition of the data competition.  

Additionally, we will conduct our own analysis on the data that has been collected. This will expand our initial findings that highlight where and how a smartwatch could be used to improve T1D management. It will also test if adding smartwatch data can improve the prediction of blood glucose, by factoring in information on activity. This could be utilised in closed-loop systems (Figure 1) and would allow them to factor activity into their algorithms. For example, if the user was to go for a walk, this system could detect that activity and then predict the drop in blood glucose levels it would cause and so reduce insulin delivery to counteract this drop. Such a system would improve T1D management and reduce the burden placed on those managing it. 

Contact details

Sam James: sam.james@bristol.ac.uk 

Miranda Armstrong: Miranda.armstrong@bristol.ac.uk 

Zahraa Abdallah: zahraa.abdallah@bristol.ac.uk