TY - JOUR
T1 - Analysis of wearable time series data in endocrine and metabolic research
AU - Grant, Azure D.
AU - Upton, Thomas J.
AU - Terry, John
AU - Smarr, Benjamin L.
AU - Zavala, Eder
PY - 2022/8/1
Y1 - 2022/8/1
N2 - 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.
AB - 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.
KW - computer algorithms
KW - hormone dynamics
KW - personalised medicine
KW - precision medicine
KW - time series analysis
KW - wearables
UR - http://www.scopus.com/inward/record.url?scp=85135417600&partnerID=8YFLogxK
U2 - 10.1016/j.coemr.2022.100380
DO - 10.1016/j.coemr.2022.100380
M3 - Article
SN - 2451-9650
VL - 25
JO - Current Opinion in Endocrine and Metabolic Research
JF - Current Opinion in Endocrine and Metabolic Research
M1 - 100380
ER -