ArticleCell reports methods2023
Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Stage-Wise IoT Solutions for Alzheimer's Disease: A Systematic Review of Detection, Monitoring, and Assistive Technologies.Sensors (Basel, Switzerland) · 2025Pooled it
- The nutritional and metabolic impact of night shift work in a real-world setting.Npj biological timing and sleep · 2026Article
- Translational applications of circadian research: connecting chronobiology to medicine.Npj biological timing and sleep · 2026Review
- Challenges of Wearable Biosensors and Ways to Overcome Them.Biosensors · 2026Review
- Circadian physiology changes with aging: age is just a number, but circadian physiology keeps time.Current opinion in physiology · 2026Article
- A deep learning framework for virtual continuous glucose monitoring and glucose prediction based on life-log data.Scientific reports · 2025Article
- Circadian clock communication during homeostasis and ageing.Nature reviews. Molecular cell biology · 2025Review
- On the imperative of including 24-h and longitudinal multidimensional physiological phenotyping in rodent models of sleep apnea.Sleep medicine · 2025Article
- The metabolic and circadian signatures of gestational diabetes in the postpartum period characterised using multiple wearable devices.Diabetologia · 2025Observational
- Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges.PLOS digital health · 2024Review
- Real World Interstitial Glucose Profiles of a Large Cohort of Physically Active Men and Women.Sensors (Basel, Switzerland) · 2024Article
- Article
- AI4FoodDB: a database for personalized e-Health nutrition and lifestyle through wearable devices and artificial intelligence.Database : the journal of biological databases and curation · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR), and heart rate variability (HRV) in 25 healthy participants over 14 days. We develop a Bayesian inference framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modeling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm for glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40%-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals. A more complex model incorporating activity, HR, and HRV explains up to 15% of additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.
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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.