Analysis of wearable time series data in endocrine and metabolic research

Azure D. Grant, Thomas J. Upton, John Terry, Benjamin L. Smarr, Eder Zavala

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Many hormones in the body oscillate with different frequencies and amplitudes, creating a dynamic environment that is essential to maintain health. In humans, disruptions to these rhythms are strongly associated with increased morbidity and mortality. While mathematical models can help us understand rhythm misalignment, translating this insight into personalised healthcare technologies requires solving additional challenges. Here, we discuss how combining minimally-invasive, high-frequency biosampling technologies with wearable devices can assist the development of hormonal surrogates. We review bespoke algorithms that can help analyse multidimensional, noisy, time series data and identify wearable signals that could constitute clinical proxies of endocrine rhythms. These techniques can support the development of computational biomarkers to support the diagnosis and management of endocrine and metabolic conditions.
Original languageEnglish
Article number100380
JournalCurrent Opinion in Endocrine and Metabolic Research
Early online date2 Jul 2022
Publication statusPublished - 1 Aug 2022


  • computer algorithms
  • hormone dynamics
  • personalised medicine
  • precision medicine
  • time series analysis
  • wearables


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