دورية أكاديمية
High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study
| العنوان: | High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study |
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| المؤلفون: | Weizhuang Zhou, Yu En Chan, Chuan Sheng Foo, Jingxian Zhang, Jing Xian Teo, Sonia Davila, Weiting Huang, Jonathan Yap, Stuart Cook, Patrick Tan, Calvin Woon-Loong Chin, Khung Keong Yeo, Weng Khong Lim, Pavitra Krishnaswamy |
| المصدر: | Journal of Medical Internet Research, Vol 24, Iss 7, p e34669 (2022) |
| بيانات النشر: | JMIR Publications, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Public aspects of medicine |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270 |
| الوصف: | BackgroundConsumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized. ObjectiveWe aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk. MethodsWe introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events. ResultsWe found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1438-8871 |
| Relation: | https://www.jmir.org/2022/7/e34669; https://doaj.org/toc/1438-8871 |
| DOI: | 10.2196/34669 |
| URL الوصول: | https://doaj.org/article/53779a435fe84936a61d6c49417d853d |
| رقم الأكسشن: | edsdoj.53779a435fe84936a61d6c49417d853d |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 14388871 |
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| DOI: | 10.2196/34669 |