# DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions

arXiv: https://arxiv.org/abs/2304.06506 · HTML: https://mlsanity.com/paper/2304.06506/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 73 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Convolutional Recurrent Neural Networks for Glucose Prediction](https://mlsanity.com/paper/1807.03043/) (2018)
- [Adherence to Personal Health Devices: A Case Study in Diabetes Management](https://mlsanity.com/paper/2006.04947/) (2020)
- [Understanding Reflection Needs for Personal Health Data in Diabetes](https://mlsanity.com/paper/2007.12761/) (2020)

## Built on by (most cited first, 15 of 15)

- [MetaboNet: The Largest Publicly Available Consolidated Dataset for Type 1 Diabetes Management](https://mlsanity.com/paper/2601.11505/) (2026)
- [Presenting DiaData for Research on Type 1 Diabetes](https://mlsanity.com/paper/2508.09160/) (2025)
- [Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes](https://mlsanity.com/paper/2506.07864/) (2025)
- [BrisT1D Dataset: Young Adults with Type 1 Diabetes in the UK using Smartwatches](https://mlsanity.com/paper/2507.17757/) (2025)
- [Benchmarking ResNet for Short-Term Hypoglycemia Classification with DiaData](https://mlsanity.com/paper/2511.02849/) (2025)
- [Glucose-ML: A collection of longitudinal diabetes datasets for development of robust AI solutions](https://mlsanity.com/paper/2507.14077/) (2025)
- [Tailoring Adverse Event Prediction in Type 1 Diabetes with Patient-Specific Deep Learning Models](https://mlsanity.com/paper/2601.14917/) (2026)
- [Optimizing the Design of an Artificial Pancreas to Improve Diabetes Management](https://mlsanity.com/paper/2402.07949/) (2024)
- [A Review on Machine Learning Approaches for the Prediction of Glucose Levels and Hypogylcemia](https://mlsanity.com/paper/2601.11615/) (2026)
- [Impact of Age Specialized Models for Hypoglycemia Classification](https://mlsanity.com/paper/2604.23732/) (2026)
- [GlucoFM-Bench: Benchmarking Time-Series Foundation Models for Blood Glucose Forecasting](https://mlsanity.com/paper/2606.06881/) (2026)
- [Personal Care Utility: Health as Everyday Infrastructure](https://mlsanity.com/paper/2606.14145/) (2026)
- [MetaboNet-Bench: A Multi-modal Benchmark for Glucose Forecasting in Type 1 Diabetes](https://mlsanity.com/paper/2606.18640/) (2026)
- [Subject-Conditioned Glucose Forecasting in Type-1 Diabetes](https://mlsanity.com/paper/2607.19006/) (2026)
- [GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes](https://mlsanity.com/paper/2607.21117/) (2026)

## Abstract (arXiv)

Objective digital data is scarce yet needed in many domains to enable research that can transform the standard of healthcare. While data from consumer-grade wearables and smartphones is more accessible, there is critical need for similar data from clinical-grade devices used by patients with a diagnosed condition. The prevalence of wearable medical devices in the diabetes domain sets the stage for unique research and development within this field and beyond. However, the scarcity of open-source datasets presents a major barrier to progress. To facilitate broader research on diabetes-relevant problems and accelerate development of robust computational solutions, we provide the DiaTrend dataset. The DiaTrend dataset is composed of intensive longitudinal data from wearable medical devices, including a total of 27,561 days of continuous glucose monitor data and 8,220 days of insulin pump data from 54 patients with diabetes. This dataset is useful for developing novel analytic solutions that can reduce the disease burden for people living with diabetes and increase knowledge on chronic condition management in outpatient settings.