ArticleScientific data2023
DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Deep learning for blood glucose prediction: Reproducibility challenges and factors affecting differential performance.PLOS digital health · 2026Article
- Causal mediation pathways in continuous postprandial glucose monitoring for type 1 diabetes patients.npj metabolic health and disease · 2026Article
- Article
- Multimodal dataset on glucose interpretation, treatment decisions and smartwatch visualisation for type 1 diabetes.Scientific data · 2026Article
- A bimodal dataset for diabetes research.Scientific data · 2026Article
- Comprehensive blood glucose level prediction from HbAScientific reports · 2026Article
- Generalized multi task learning framework for glucose forecasting and hypoglycemia detection using simulation to reality.NPJ digital medicine · 2025Article
- Mealtime prediction using wearable insulin pump data to support diabetes management.Scientific reports · 2024Article
- Deep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management.IEEE open journal of engineering in medicine and biology · 2024Article
- T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus.Scientific data · 2023Article
- Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.