Dataset Information

Analysis of wearable time series data in endocrine and metabolic research.

ABSTRACT: 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.

SUBMITTER: Grant AD

PROVIDER: S-EPMC9823090 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Analysis of wearable time series data in endocrine and metabolic research.

Grant Azure D AD Upton Thomas J TJ Terry John R JR Smarr Benjamin L BL Zavala Eder E

Current opinion in endocrine and metabolic research 20220801

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 biosa ...[more]

PMID: 36632470

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Dataset Information

Analysis of wearable time series data in endocrine and metabolic research.

Publications

Analysis of wearable time series data in endocrine and metabolic research.

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