Prospective Validation of 2B-Cool : Integrating Wearables and Individualized Predictive Analytics to Reduce Heat Injuries.
Introduction: An 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 nonintrusi...
| Publicado en: | Medicine & Science in Sports & Exercise Vol. 55; no. 4; pp. 751 - 765 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts randomized controlled trial Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Apr2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162413485&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162413485 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01959131 4DP jtl: Medicine & Science in Sports & Exercise issn: 01959131 maglogo: N pubinfo: dt: Apr2023 vid: 55 iid: 4 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 162413485 162413485 162413485 10.1249/MSS.0000000000003093 162413485 ppf: 751 ppct: 14 formats: tig: atl: Prospective Validation of 2B-Cool : Integrating Wearables and Individualized Predictive Analytics to Reduce Heat Injuries. aug: au: LAXMINARAYAN, SRINIVAS HORNBY, SAMANTHA BELVAL, LUKE N. GIERSCH, GABRIELLE E. W. MORRISSEY, MARGARET C. CASA, DOUGLAS J. REIFMAN, JAQUES affil: Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD sug: subj: Wearable Sensors Equipment and Supplies Smartphone Machine Learning Heat Stress Disorders Prevention and Control Body Temperature Sensitivity and Specificity Human Male Female Descriptive Statistics Comparative Studies Prediction Models Crossover Design Treadmills Algorithms Environment, Controlled Randomized Controlled Trials Adolescence Young Adult Vital Signs Heart Rate Adolescent: 13-18 years Male Female ab: Introduction: An 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. Methods: We 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. Results: We 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. Conclusion: 2B-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. pubtype: Academic Journal doctype: research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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