Novel Analytic Methods Needed for Real-Time Continuous Core Body Temperature Data. (January 2017)

• Record Type: Journal Article
• Authors: Hertzberg, Vicki; Mac, Valerie; Elon, Lisa; Mutic, Nathan; Mutic, Abby; Peterman, Katherine; Tovar-Aguilar, J. Antonio; Economos, Eugenia; Flocks, Joan; McCauley, Linda
• Is Part Of: Western journal of nursing research. Volume 39:Number 1(2017)
• Page Start: 95
• Page End: 111
• Publication Date: 2017-01
• Subjects: smoothing -- functional data analysis -- core body temperature -- heat-related illness -- occupational epidemiology -- farmworker -- heat stress -- LOESS; Nursing -- Research -- Periodicals; Nursing -- Periodicals; Nursing -- Research -- West (U.S.) -- Periodicals; Nursing -- West (U.S.) -- Periodicals; Nursing -- Periodicals; Nursing Research -- Periodicals; Soins infirmiers -- Recherche -- Périodiques; Soins infirmiers -- Périodiques; Soins infirmiers -- Recherche -- États-Unis (Ouest) -- Périodiques; Soins infirmiers -- États-Unis (Ouest) -- Périodiques; Soins infirmiers; Recherche; États-Unis (Ouest); Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 610.730702
• DOI: 10.1177/0193945916673058
• Languages: English
• ISSNs: 0193-9459

Abstract

Affordable measurement of core body temperature (Tc ) in a continuous, real-time fashion is now possible. With this advance comes a new data analysis paradigm for occupational epidemiology. We characterize issues arising after obtaining Tc data over 188 workdays for 83 participating farmworkers, a population vulnerable to effects of rising temperatures due to climate change. We describe a novel approach to these data using smoothing and functional data analysis. This approach highlights different data aspects compared with describing Tc at a single time point or summaries of the time course into an indicator function (e.g., did Tc ever exceed 38 °C, the threshold limit value for occupational heat exposure). Participants working in ferneries had significantly higher Tc at some point during the workday compared with those working in nurseries, despite a shorter workday for fernery participants. Our results typify the challenges and opportunities in analyzing Big Data streams from real-time physiologic monitoring.