ArticleNPJ digital medicine2025
Human-machine co-adaptation to automated insulin delivery: a randomised clinical trial using digital twin technology.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05610111 (Adaptive Biobehavioral Control), which is not on this map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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.
Adaptive Biobehavioral Control (ABC) in a Closed-Loop System: A Randomized Crossover Clinical Trial
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Regulatory Adoption of AI, ML, Computational Modeling & Simulation in In-Silico Clinical Trials for Medical Devices: A Systematic Review.Therapeutic innovation & regulatory science · 2026Pooled it
- Hybrid Digital Twin Framework for Personalized Diabetes Management Using Mathematical Modelling and Machine Learning.Diagnostics (Basel, Switzerland) · 2026Article
- From narratives to numbers and back: Assessing the psychosocial aspects of diabetes in the era of high technology with emerging qualitative and quantitative methodologies.Diabetic medicine : a journal of the British Diabetic Association · 2026Review
- Exercise as a Programmable Regulator of Mitophagy Sensitivity in Aging Muscle and Age-Related Disease.IUBMB life · 2026Review
- Applications of human-machine collaborative decision-making: A review of research with recent developments.iScience · 2026Review
- Artificial Intelligence in Type 1 Diabetes Management: A Scoping Review of Randomised Controlled Trials.Diabetes, obesity & metabolism · 2026Article
- The Potential of Digital Twins for Pediatric Rare Diseases.CPT: pharmacometrics & systems pharmacology · 2026Review
- Cross-fusion of digital twins and artificial intelligence in diabetes: from mechanistic elucidation to full-cycle precision management.Frontiers in endocrinology · 2026Review
- Artificial Intelligence in Diabetes Care: Applications, Challenges, and Opportunities Ahead.Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists · 2025Review
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- Essential Concepts in Artificial Intelligence: A Guide for Pediatric Providers.Children (Basel, Switzerland) · 2025Review
- Personalized Nutrition in Pediatric Chronic Diseases.Metabolites · 2025Review
- 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
10 authors.
Funding
Abstract
Most automated insulin delivery (AID) algorithms do not adapt to the changing physiology of their users, and none provide interactive means for user adaptation to the actions of AID. This randomised clinical trial tested human-machine co-adaptation to AID using new 'digital twin' replay simulation technology. Seventy-two individuals with T1D completed the 6-month study. The two study arms differed by the order of administration of information feedback (widely used metrics and graphs) and in silico co-adaptation routine, which: (i) transmitted AID data to a cloud application; (ii) mapped each person to their digital twin; (iii) optimized AID control parameters bi-weekly, and (iv) enabled users to experiment with what-if scenarios replayed via their own digital twins. In silico co-adaptation improved the primary outcome, time-in-range (3.9-10 mmol/L), from 72 to 77 percent (p < 0.01) and reduced glycated haemoglobin from 6.8 to 6.6 percent. Information feedback did not have additional effect to AID alone. (Clinical Trials Registration: NCT05610111 (November 10, 2022)).
Identifiers
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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.