Wearables Detect Malaria Early in a Controlled Human-Infection Study

Chaudhury, Sidhartha; Yu, Chenggang; Liu, Ruifeng; Kumar, Kamal; Hornby, Samantha; Duplessis, Christopher; Sklar, Joel M; Epstein, Judith E.; Reifman, Jaques

doi:10.1109/tbme.2021.3137756

Cited by 7 publications

(5 citation statements)

References 19 publications

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“…The 2B-Cool algorithms are agnostic of the vital sign–measuring device. Nevertheless, because vital signs measured by wearable devices may be contaminated by noise artifacts (20), we assessed the robustness of 2B-Cool ’s estimation algorithm with HR measured by different types of devices and with the unavailability of T S data. To characterize the effect of error measurements in HR collected with the Samsung Gear S3, we compared them against those of the widely used Polar H7 chest strap device.…”

Section: Resultsmentioning

confidence: 99%

“…The hardware consisted of a Samsung Gear S3 smartwatch paired with a Samsung Note 4 smartphone, both of which were specially configured for this study. We selected the Samsung Gear S3 because it provides direct access to the sensor data, without requiring third-party involvement or upload to a cloud service, and because we previously carried out an in-house assessment of its sensors' accuracy (12,20). In this assessment, healthy volunteers simultaneously wore both the Samsung Gear S3 and the gold standard devices under everyday ambulatory conditions, where we found that the median absolute difference was ≤5 bpm against the Polar H7 HR sensor, ≤1.30°C against the iButton skin temperature sensor (iButtonLink LLC, Whitewater, WI), and ≤0.032g against the ActiGraph wGT3X-BT accelerometer device (ActiWatch LLC, Pensacola, FL) (20).…”

Section: Study Datamentioning

confidence: 99%

“…We conducted an exertional heat stress study in an environmentally controlled chamber at the University of Connecticut, where we recruited 22 healthy, young men and women (11 each): age = 20.31 ± 2.01 (18)(19)(20)(21)(22)(23)(24)(25) yr, height = 1.70 ± 0.11 (1.55-1.94) m, weight = 67.25 ± 13.56 (47.84-93.30) kg, body mass index = 22.97 ± 2.60 (19.16-28.65) kg•m −2 , and VO 2max = 46.81 ± 5.28 (36.34-56.54) mL•kg −1 ⋅min −1 ; mean ± SD (range, min-max). We screened the subjects for health problems, prior heat illness, medications, and pregnancy, which could affect the study results.…”

Section: Study Datamentioning

confidence: 99%

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Prospective Validation of 2B-Cool: Integrating Wearables and Individualized Predictive Analytics to Reduce Heat Injuries

Laxminarayan

Hornby

Belval

et al. 2022

Medicine &Amp; Science in Sports &Amp; Exercise

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IntroductionAn uncontrollably rising core body temperature (TC) is an indicator of an impending exertional heat illness. However, measuring TC invasively in field settings is challenging. By contrast, wearable sensors combined with machine-learning algorithms can continuously monitor TC nonintrusively. Here, we prospectively validated 2B-Cool, a hardware/software system that automatically learns how individuals respond to heat stress and provides individualized estimates of TC, 20-min ahead predictions, and early warning of a rising TC.MethodsWe performed a crossover heat stress study in an environmental chamber, involving 11 men and 11 women (mean ± SD age = 20 ± 2 yr) who performed three bouts of varying physical activities on a treadmill over a 7.5-h trial, each under four different clothing and environmental conditions. Subjects wore the 2B-Cool system, consisting of a smartwatch, which collected vital signs, and a paired smartphone, which housed machine-learning algorithms and used the vital sign data to make individualized real-time forecasts. Subjects also wore a chest strap heart rate sensor and a rectal probe for comparison purposes.ResultsWe observed very good agreement between the 2B-Cool forecasts and the measured TC, with a mean bias of 0.16°C for TC estimates and nearly 75% of measurements falling within the 95% prediction intervals of ±0.62°C for the 20-min predictions. The early-warning system results for a 38.50°C threshold yielded a 98% sensitivity, an 81% specificity, a prediction horizon of 35 min, and a false alarm rate of 0.12 events per hour. We observed no sex differences in the measured or predicted peak TC.Conclusion2B-Cool provides early warning of a rising TC with a sufficient lead time to enable clinical interventions and to help reduce the risk of exertional heat illness.

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Section: Resultsmentioning

confidence: 99%

Section: Study Datamentioning

confidence: 99%

Section: Study Datamentioning

confidence: 99%

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Prospective Validation of 2B-Cool: Integrating Wearables and Individualized Predictive Analytics to Reduce Heat Injuries

Laxminarayan

Hornby

Belval

et al. 2022

Medicine &Amp; Science in Sports &Amp; Exercise

Self Cite

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“…Wearable devices have found increased applications in recent years [ 88 ]. A comprehensive understanding of MPXV’s current clinical manifestations [ 89 ] and the integration of this information with smartphone apps and smartwatches might be helpful in developing screening systems for presymptomatic cases.…”

Section: Conclusion and Prospectsmentioning

confidence: 99%

Current and Perspective Sensing Methods for Monkeypox Virus

et al. 2022

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The outbreak of the monkeypox virus (MPXV) in non-endemic countries is an emerging global health threat and may have an economic impact if proactive actions are not taken. As shown by the COVID-19 pandemic, rapid, accurate, and cost-effective virus detection techniques play a pivotal role in disease diagnosis and control. Considering the sudden multicountry MPXV outbreak, a critical evaluation of the MPXV detection approaches would be a timely addition to the endeavors in progress for MPXV control and prevention. Herein, we evaluate the current MPXV detection methods, discuss their pros and cons, and provide recommended solutions to the problems. We review the traditional and emerging nucleic acid detection approaches, immunodiagnostics, whole-particle detection, and imaging-based MPXV detection techniques. The insights provided in this article will help researchers to develop novel techniques for the diagnosis of MPXV.

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“…Many recent efforts propose machine learning classifiers for the within-individual detection of specific, acute illnesses using wearable device data [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Other works have investigated using wearables to monitor population-level changes corresponding to influenza-like illnesses (ILI) [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach

Kasl,

Keeler Bruce,

Hartogensis

et al. 2024

Sensors

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Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants’ wearable device data and participants’ responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants’ fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.

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Wearables Detect Malaria Early in a Controlled Human-Infection Study

Cited by 7 publications

References 19 publications

Prospective Validation of 2B-Cool: Integrating Wearables and Individualized Predictive Analytics to Reduce Heat Injuries

Prospective Validation of 2B-Cool: Integrating Wearables and Individualized Predictive Analytics to Reduce Heat Injuries

Current and Perspective Sensing Methods for Monkeypox Virus

Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach

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