Wearable sensors enable personalized predictions of clinical laboratory measurements

Dunn, Jessilyn; Kidziński, Łukasz; Runge, Ryan; Witt, Daniel R.; Hicks, Jennifer L.; Rose, Sophia Miryam Schüssler‐Fiorenza; Li, Xiao; Bahmani, Amir; Delp, Scott L.; Hastie, Trevor; Snyder, M

doi:10.1038/s41591-021-01339-0

Cited by 132 publications

(91 citation statements)

References 44 publications

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“…Wearable devices such as smartwatches have the potential to monitor individuals continuously in real time and thus provide early detection of respiratory illnesses and other infections 6 – 11 . These devices can collect different types of physiological data, such as heart rate, step counts, sleep and temperature.…”

Section: Mainmentioning

confidence: 99%

Real-time alerting system for COVID-19 and other stress events using wearable data

Alavi

Bogu

Wang

et al. 2021

Nat Med

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Early detection of infectious diseases is crucial for reducing transmission and facilitating early intervention. In this study, we built a real-time smartwatch-based alerting system that detects aberrant physiological and activity signals (heart rates and steps) associated with the onset of early infection and implemented this system in a prospective study. In a cohort of 3,318 participants, of whom 84 were infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), this system generated alerts for pre-symptomatic and asymptomatic SARS-CoV-2 infection in 67 (80%) of the infected individuals. Pre-symptomatic signals were observed at a median of 3 days before symptom onset. Examination of detailed survey responses provided by the participants revealed that other respiratory infections as well as events not associated with infection, such as stress, alcohol consumption and travel, could also trigger alerts, albeit at a much lower mean frequency (1.15 alert days per person compared to 3.42 alert days per person for coronavirus disease 2019 cases). Thus, analysis of smartwatch signals by an online detection algorithm provides advance warning of SARS-CoV-2 infection in a high percentage of cases. This study shows that a real-time alerting system can be used for early detection of infection and other stressors and employed on an open-source platform that is scalable to millions of users.

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Section: Mainmentioning

confidence: 99%

Real-time alerting system for COVID-19 and other stress events using wearable data

Alavi

Bogu

Wang

et al. 2021

Nat Med

Self Cite

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“…PGHD represent a low burden direct connection to the patient journey, and such data have already been demonstrated to be a valuable component of models that predict health-relevant outcomes [ 27 , 28 ]. We present a 2-phase approach for predicting longitudinal deterioration in depression status.…”

Section: Discussionmentioning

confidence: 99%

Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study

Makhmutova¹,

Kainkaryam²,

Ferreira³

et al. 2022

JMIR Mhealth Uhealth

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Background In 2017, an estimated 17.3 million adults in the United States experienced at least one major depressive episode, with 35% of them not receiving any treatment. Underdiagnosis of depression has been attributed to many reasons, including stigma surrounding mental health, limited access to medical care, and barriers due to cost. Objective This study aimed to determine if low-burden personal health solutions, leveraging person-generated health data (PGHD), could represent a possible way to increase engagement and improve outcomes. Methods Here, we present the development of PSYCHE-D (Prediction of Severity Change-Depression), a predictive model developed using PGHD from more than 4000 individuals, which forecasts the long-term increase in depression severity. PSYCHE-D uses a 2-phase approach. The first phase supplements self-reports with intermediate generated labels, and the second phase predicts changing status over a 3-month period, up to 2 months in advance. The 2 phases are implemented as a single pipeline in order to eliminate data leakage and ensure results are generalizable. Results PSYCHE-D is composed of 2 Light Gradient Boosting Machine (LightGBM) algorithm–based classifiers that use a range of PGHD input features, including objective activity and sleep, self-reported changes in lifestyle and medication, and generated intermediate observations of depression status. The approach generalizes to previously unseen participants to detect an increase in depression severity over a 3-month interval, with a sensitivity of 55.4% and a specificity of 65.3%, nearly tripling sensitivity while maintaining specificity when compared with a random model. Conclusions These results demonstrate that low-burden PGHD can be the basis of accurate and timely warnings that an individual’s mental health may be deteriorating. We hope this work will serve as a basis for improved engagement and treatment of individuals experiencing depression.

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“…2) Working Preciseness: The variability among different people would require more sophisticated and powerconsuming control circuits to realize the accurate electrical stimulation. [146][147][148] Developing those circuits and improving the energy conversion efficiency of portable TENGs are possible future directions. [149,150] 3) Human Trial: Current investigations of TENG-based wound healing are mainly based on in vitro and ex vivo experiments, i.e., rats and Petri dishes.…”

Section: ) Device Miniaturization and Durability: To Practically Adaptmentioning

confidence: 99%

Triboelectric Nanogenerators for Self‐Powered Wound Healing

Xiao

Nashalian

Libanori

et al. 2021

Adv Healthcare Materials

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Wound healing, one of the most complex processes in the human body, involves the spatial and temporal synchronization of a variety of cell types with distinct roles. Slow or nonhealing skin wounds have potentially life-threatening consequences, ranging from infection to scar, clot, and hemorrhage. Recently, the advent of triboelectric nanogenerators (TENGs) has brought about a plethora of self-powered wound healing opportunities, owing to their pertinent features, including wide range choices of constitutive biocompatible materials, simple fabrication, portable size, high output power, and low cost. Herein, a comprehensive review of TENGs as an emerging biotechnology for wound healing applications is presented and covered from three unique aspects: electrical stimulation, antibacterial activity, and drug delivery. To provide a broader context of TENGs applicable to wound healing applications, state-of-the-art designs are presented and discussed in each section. Although some challenges remain, TENGs are proving to be a promising platform for human-centric therapeutics in the era of Internet of Things. Consequently, TENGs for wound healing are expected to provide a new solution in wound management and play an essential role in the future of point-of-care interventions.

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Wearable sensors enable personalized predictions of clinical laboratory measurements

Cited by 132 publications

References 44 publications

Real-time alerting system for COVID-19 and other stress events using wearable data

Real-time alerting system for COVID-19 and other stress events using wearable data

Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study

Triboelectric Nanogenerators for Self‐Powered Wound Healing

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