ArticleScientific data2025
A Longitudinal Multimodal Dataset of Type 1 Diabetes.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Article
- Multimodal dataset on glucose interpretation, treatment decisions and smartwatch visualisation for type 1 diabetes.Scientific data · 2026Article
- Coefficient of Variation to Assess the Reproducibility of Meal-Induced Glycemic Responses: Development of a Clustering Algorithm.JMIR diabetes · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
People living with Type 1 Diabetes (PwT1D) must continuously monitor blood glucose levels and make critical clinical and safety-related decisions multiple times a day to maintain glycaemic control within recommended ranges. While significant efforts have been made to develop algorithms that assist PwT1D in managing blood glucose more effectively, access to automated insulin delivery (AID) systems remains highly variable across the world. Moreover, there is a lack of publicly available, comprehensive datasets necessary for developing algorithms to support scenarios where AID systems revert to manual mode. This study addresses this gap by providing a detailed, multimodal dataset encompassing five key aspects: blood glucose levels; basal and bolus insulin dosages; nutritional intake (carbohydrates, protein, fat, and fibre content); physical activity (step count, active calories, distance covered, MET, and intensity level); and sleep patterns. The dataset includes longitudinal (3-month) real-world data collected from 17 PwT1D participants. By making this resource available, the study aims to advance algorithm development and improve diabetes management, particularly in settings where AID technology is less accessible.
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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.