This is a collection of links to publicly available continuous glucose monitoring (CGM) data.
CGMs are small wearable devices that allow to measure glucose levels continuously throughout the day, with some meters taking measurements as often as every 5 min. For the head start on CGM data analyses, check out our R package iglu.
This collection follows the style of Mike Love's awesome-multi-omics and Sean Davis' awesome-single-cell repos, although the latter are collections of methods rather than dataset links.
The original list was assembled by Mary Martin, Elizabeth Chun, David Buchanan, Eric Wang, and Sangaman Senthil as part of their Aggie Research Project under the supervision of Dr. Irina Gaynanova. Contributions are welcome.
To cite current version 1.1.0, use
Mary Martin, Elizabeth Chun, David Buchanan, Rucha Bhat, Shaun Cass, Eric Wang, Sangaman Senthil & Irina Gaynanova. (2021, April 27). irinagain/Awesome-CGM: List of public CGM datasets (Version v1.1.0). Zenodo.
Below is a list overview of all datasets with the links, the same list in a table format can be seen here. Click the name of a data set for a link to download the CGM data, pre-processing scripts, and covariate information, as well as any additional dataset-specific use agreements.
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- The purpose of this study was to determine whether the use of continuous glucose monitoring (CGM) without blood glucose monitoring (BGM) measurements is as safe and effective as using CGM with BGM in adults (25-40) with type 1 diabetes. The total sample size was 225 participants. The Dexcom G4 was used to continuously monitor glucose levels for a span of 6 months.
- Found by Mary Martin. CGM Processor by David Buchanan, Elizabeth Chun. Updated R processor by Shaun Cass. Uploaded by Mary Martin, Elizabeth Chun.
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- This study was designed to test a closed-loop control-to-range artificial pancreas (AP) system. There are two phases to this study. Within Phase 1, there were various stages of this study starting with 0-3 weeks of practice with CGM. This was followed by 2 weeks using the study pump together with CGM known as sensor augmented pump (SAP) therapy. This was used as a baseline and followed by 2 weeks of overnight only closed loop control (CLC) and 2 weeks of 24/7 CLC. During the CLC weeks, insulin was administered by the AP system. Phase 2 continued with only 14 patients - physician’s choice. These 14 patients used the CLC system 24/7 for five additional months.
- Found by Elizabeth Chun. CGM Processor by David Buchanan, Elizabeth Chun. Uploaded by Elizabeth Chun, Mary Martin.
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- This study was designed as a pilot study to analyze use of a CGM for children with diabetes. The subjects first established a baseline during a week blinded use, followed by at home use for 3 months.
- Found by Eric Wang. CGM Processor by David Buchanan, Elizabeth Chun. Enhanced CGM processing by Rucha Bhat. Uploaded by Elizabeth Chun, Mary Martin.
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- This study focused on the use of the GlucoWatch G2 Biographer as a tool to help in diabetes care. The 200 subjects were randomly assigned to test (CGM) or control (self-monitoring blood glucose). At the end of the study duration, A1c measurements were used to compare the two groups.
- Found by Eric Wang. Uploaded by Elizabeth Chun, Mary Martin.
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- This study focused on the use of wearable devices in a non clinical setting. There are nine type 1 diabetes patients. A large variety of data other than CGM data was collected for this study, designed for research on correlations between glucose levels and physiological measures such as ECG.
- Found by Elizabeth Chun. CGM Processor by David Buchanan, Elizabeth Chun. Uploaded by Elizabeth Chun, Mary Martin.
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- This study was designed to test CGM as a technology to assist in diabetes care. The randomized trial was intended to determine if CGM usage had a positive effect on diabetes management. The total subjects were split into two cohorts based on A1c results, with one cohort having initial A1c measurements from 7-10% and the second cohort of those with A1c levels <7%. Within each cohort, subjects were randomly assigned to a test (CGM) or control group.
- Found by David Buchanan. CGM Processor by David Buchanan, Elizabeth Chun. Uploaded by Elizabeth Chun, Mary Martin.
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- The purpose of this study was to find out how often low blood sugar (hypoglycemia) occurs during the night after exercise in late afternoon for children aged 10 to 18 with type 1 diabetes. The total sample size was 50 participants. The OneTouch Ultra Meter was used to continuously monitor glucose levels during two seperate 24 hours periods.
- Found by Eric Wang. CGM Processor by David Buchanan, Elizabeth Chun. Uploaded by Mary Martin, Elizabeth Chun.
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- The purpose of this study was to identify factors associated with severe hypoglycemia in older adults (60+) with type 1 diabetes. The total sample size was 200 participants: 100 cases, 100 control. The Dexcom SEVEN PLUS was used to continuously monitor glucose levels for a span of 2 weeks.
- Found by Mary Martin. CGM Processor by Sangaman Senthil, Elizabeth Chun. Uploaded by Mary Martin, Elizabeth Chun.
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- This study analyzes how blood glucose fluctuates in healthy individuals by using a CGM to monitor glucose. Standardized meals (breakfast only) were given to a subset of patients in order to monitor the effect of meals on the glucose readings of healthy individuals. The subjects in this study had no prior diabetes diagnosis.
- Found by Elizabeth Chun. CGM Processor by David Buchanan, Sangaman Senthil, Elizabeth Chun. Uploaded by Elizabeth Chun, Mary Martin.
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- This study was done on an animal model, specifically pigs, in order to study sensor placement and the corresponding effects on CGM data. In order to simulate meals, glucose infusions were given to non-diabetic, anesthetized pigs through IV.
- Found by Elizabeth Chun. Uploaded by Elizabeth Chun, Mary Martin.
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- This repo is a python implementation of the FDA-approved UVa/Padova Simulator for research purposes. It is "reinforcement-learning-ready", with a simulation enviroment which follows OpenAI gym and rllab APIs.
- Found by David Buchanan.
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- The AIDA simulator is intended for simulating the effects on the blood glucose profile of changes in insulin and diet for a typical insulin-dependent (type 1) diabetic patient.
- Found by David Buchanan