Wearable technology and systems modeling for personalized chronotherapy
| Autor: | Eder Zavala, Dae Wook Kim, Jae Kyoung Kim |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
Computer science business.industry Applied Mathematics medicine.medical_treatment Wearable computer Systems modeling General Biochemistry Genetics and Molecular Biology Chronotherapies Chronotherapy (treatment scheduling) Computer Science Applications 03 medical and health sciences 0302 clinical medicine Human–computer interaction Modeling and Simulation Drug Discovery medicine Personalized medicine business Internal time Clinical scenario 030217 neurology & neurosurgery Wearable technology 030304 developmental biology |
| Zdroj: | Current Opinion in Systems Biology. 21:9-15 |
| ISSN: | 2452-3100 |
| DOI: | 10.1016/j.coisb.2020.07.007 |
| Popis: | Chronotherapy is a pharmaceutical intervention that considers the patient's internal circadian time to adjust dosing time. Although it can dramatically improve drug efficacy and reduce toxicity, the large variability in internal time across and within individuals has prevented chronotherapies from progressing beyond clinical trials. To translate chronotherapy developments into a real-world outpatient clinical scenario, a personalized characterization and analysis of a patient's internal time is essential. Here, we describe recent advances in wearable technology that enable real-time high-resolution tracking of circadian and ultradian rhythms. We discuss how integrating wearable data into analysis platforms including systems modeling and machine learning can pave the way toward personalized adaptive chronotherapy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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