ArticleJournal of the Royal Society, Interface2018
Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data.
Article in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Relationships between rest-activity rhythms, cancer-related fatigue, and quality of life: Results from a time-restricted eating randomized controlled trial.Chronobiology international · 2026Trial
- Hierarchical heated markov modeling for synthesizing activity data from wearable devices.Computational and structural biotechnology journal · 2026Article
- Augmenting Circadian Biology Research With Data Science.Journal of biological rhythms · 2025Review
- Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms.Data science in science · 2025Article
- Validating CircaCP: a generic sleep-wake cycle detection algorithm for unlabelled actigraphy data.Royal Society open science · 2024Article
- Digital Remote Monitoring Using an mHealth Solution for Survivors of Cancer: Protocol for a Pilot Observational Study.JMIR research protocols · 2024Observational
- Molecular characterization of the circadian clock in patients with Parkinson's disease-CLOCK4PD Study protocol.PloS one · 2024Article
- A hidden Markov modeling approach combining objective measure of activity and subjective measure of self-reported sleep to estimate the sleep-wake cycle.Journal of applied statistics · 2024Article
- Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data.Journal of the Royal Society, Interface · 2023Article
- Cross-sectional and Prospective Associations of Rest-Activity Rhythms With Body Mass Index in Older Men: A Novel Analysis Using Harmonic Hidden Markov Models.Journal of biological rhythms · 2023Article
- Human Activity Recognition with an HMM-Based Generative Model.Sensors (Basel, Switzerland) · 2023Article
- Measuring Daily Activity Rhythms in Young Adults at Risk of Affective Instability Using Passively Collected Smartphone Data: Observational Study.JMIR formative research · 2022Article
- Digital circadian and sleep health in individual hospital shift workers: A cross sectional telemonitoring study.EBioMedicine · 2022Article
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Circadian disruption and cancer- and treatment-related symptoms.Frontiers in oncology · 2022Review
- Circadian and chemotherapy-related changes in urinary modified nucleosides excretion in patients with metastatic colorectal cancer.Scientific reports · 2021Article
- Identifying the Recurrence of Sleep Apnea Using A Harmonic Hidden Markov Model.The annals of applied statistics · 2021Article
- Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions.Scientific reports · 2021Article
- Biological Rhythm and Chronotype: New Perspectives in Health.Biomolecules · 2021Review
- Telemonitored Human Circadian Temperature Dynamics During Daily Routine.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Wearable computing devices allow collection of densely sampled real-time information on movement enabling researchers and medical experts to obtain objective and non-obtrusive records of actual activity of a subject in the real world over many days. Our interest here is motivated by the use of activity data for evaluating and monitoring the circadian rhythmicity of subjects for research in chronobiology and chronotherapeutic healthcare. In order to translate the information from such high-volume data arising we propose the use of a Markov modelling approach which (i) naturally captures the notable square wave form observed in activity data along with heterogeneous ultradian variances over the circadian cycle of human activity, (ii) thresholds activity into different states in a probabilistic way while respecting time dependence and (iii) gives rise to circadian rhythm parameter estimates, based on probabilities of transitions between rest and activity, that are interpretable and of interest to circadian research.
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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.