ArticleNPJ digital medicine2024
A physiologically-based digital twin for alcohol consumption-predicting real-life drinking responses and long-term plasma PEth.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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.
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Who cites it
9 citing papers in PubMed.
- A digital twin-enhanced decision support system improves time-in-range in type 1 diabetes: a randomized clinical trial.Scientific reports · 2025Trial
- Steatotic liver disease trajectory: A holistic EASL guidance on the multidisciplinary approach to screening and patient management.JHEP reports : innovation in hepatology · 2026Review
- A digital twin framework for forensic reconstruction of alcohol intake via fast and slow metabolite kinetics.Scientific reports · 2026Article
- The patterns of relapse and abstinence: using machine learning to identify a multidimensional signature of long-term outcome after inpatient alcohol withdrawal treatment.Frontiers in psychiatry · 2026Article
- Clinical trial design, biomarkers and end points in metabolic and alcohol-related liver disease.Nature reviews. Gastroenterology & hepatology · 2025Review
- A scoping review of human digital twins in healthcare applications and usage patterns.NPJ digital medicine · 2025Article
- Analysis of Flavor Differences BetweenFoods (Basel, Switzerland) · 2025Article
- MetALD: New Perspectives on an Old Overlooked Disease.Liver international : official journal of the International Association for the Study of the Liver · 2025Review
- The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact.NPJ digital medicine · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Alcohol consumption is associated with a wide variety of preventable health complications and is a major risk factor for all-cause mortality in the age group 15-47 years. To reduce dangerous drinking behavior, eHealth applications have shown promise. A particularly interesting potential lies in the combination of eHealth apps with mathematical models. However, existing mathematical models do not consider real-life situations, such as combined intake of meals and beverages, and do not connect drinking to clinical markers, such as phosphatidylethanol (PEth). Herein, we present such a model which can simulate real-life situations and connect drinking to long-term markers. The new model can accurately describe both estimation data according to a χ
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.