Accuracy of an estimated core temperature algorithm for agricultural workers

Jared Egbert, Jennifer Krenz, Paul D Sampson, Jihoon Jung, Miriam Calkins, Kai Zhang, Pablo Palmández, Paul Faestel, June T Spector, Jared Egbert, Jennifer Krenz, Paul D Sampson, Jihoon Jung, Miriam Calkins, Kai Zhang, Pablo Palmández, Paul Faestel, June T Spector

Abstract

There is a substantial burden of occupational health effects from heat exposure. We sought to assess the accuracy of estimated core body temperature (CBTest) derived from an algorithm that uses sequential heart rate and initializing CBT,1 compared with gastrointestinal temperature measured using more invasive ingestible sensors (CBTgi), among outdoor agricultural workers. We analyzed CBTest and CBTgi data from Washington State, USA, pear and apple harvesters collected across one work shift in 2015 (13,413 observations, 35 participants) using Bland Altman methods. The mean (standard deviation, range) CBTgi was 37.7 (0.4, 36.5-39.4)°C. Overall CBT bias (limits of agreement) was -0.14 (±0.76)°C. Biases ranged from -0.006 to -0.75 °C. The algorithm, which does not require the use of ingestible sensors, may be a practical tool in research among groups of workers for evaluating the effectiveness of interventions to prevent adverse occupational heat health effects.

Keywords: Agricultural workers; core body temperature; gastrointestinal temperature; heat stress; heat-related illness; physiological strain index.

Conflict of interest statement

Disclosure statement

The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention. Its contents, including any opinions and/or conclusions expressed, are solely those of the authors. No potential competing interest was reported by the authors.

Figures

Figure 1.
Figure 1.
Scatter plots (i,iv), Bland-Altman plots (ii,v), and histograms (iii,vi) of differences between observed (gastrointestinal) and estimated (algorithm) (a) core body temperatures (CBTs) and (b) physiological strain index (PSI) based on observed (gastrointestinal) and estimated (algorithm) CBTs using: (i–iii) default 37.1 °C; and (iv–vi) estimated baseline CBT (aural temperature +0.27 °C). Scatter plots include a best fit regression line; Bland-Altman plots include dotted lines representing bias and limits of agreement.

References

    1. Buller MJ, Tharion WJ, Cheuvront SN, et al. Estimation of human core temperature from sequential heart rate observations. Physiol Meas. 2013; 34(7):781–798. doi:10.1088/0967-3334/34/7/781.
    1. Jacklitsch B, Williams WJ, Musolin K, Coca A, Kim J-H, Turner N. 2016. NIOSH criteria for a recommended standard: Occupational exposure to heat and hot environments. Cincinnati, OH: NIOSH. . Accessed 20 Sept 2021.
    1. Gubernot DM, Anderson GB, Hunting KL. Characterizing occupational heat-related mortality in the United States, 2000–2010: an analysis using the census of fatal occupational injuries database. Am J Ind Med. 2015;58(2):203–211. doi:10.1002/ajim.22381.
    1. Heinzerling A, Laws RL, Frederick M, et al. Risk factors for occupational heat-related illness among California workers, 2000–2017. Am J Ind Med. 2020; 63(12):1145–1154. doi:10.1002/ajim.23191.
    1. Hesketh M, Wuellner S, Robinson A, Adams D, Smith C, Bonauto D. Heat related illness among workers in Washington State: A descriptive study using workers’ compensation claims, 2006–2017. Am J Ind Med. 2020; 63(4):300–311. doi:10.1002/AJIM.23092.
    1. Binazzi A, Levi M, Bonafede M, et al. Evaluation of the impact of heat stress on the occurrence of occupational injuries: Meta-analysis of observational studies. Am J Ind Med. 2019;62(3):233–243. doi:10.1002/ajim.22946.
    1. IPCC. 2014. Climate change 2014 synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 1–151. . Accessed 20 Sept 2021.
    1. Spector JT, Masuda YJ, Wolff NH, Calkins M, Seixas N. Heat exposure and occupational injuries: Review of the literature and implications. Curr Environ Health Rep. 2019;6(4):286–296. doi:10.1007/s40572-019-00250-8.
    1. Chicas R, Xiuhtecutli N, Elon L, et al. Cooling interventions among agricultural workers: A pilot study. Workplace Health Saf. 2021;69(7):315–322. doi:10.1177/2165079920976524.
    1. Krenz J, Santos E, Torres E, et al. The multi-level heat education and awareness tools [HEAT] intervention study for farmworkers: Rationale and methods. Contemp Clin Trials Commun. 2021;22:100795. doi:10.1016/J.CONCTC.2021.100795.
    1. Lam M, Krenz J, Palmandez P, et al. Identification of barriers to the prevention and treatment of heat-related illness in Latino farmworkers using activity-oriented, participatory rural appraisal focus group methods. BMC Public Health. 2013;13:1004 doi:10.1186/1471-2458-13-1004.
    1. Wegman DH, Apelqvist J, Bottai M, Work Health and Efficiency (WE) Program Working Group, et al. Intervention to diminish dehydration and kidney damage among sugarcane workers. Scand J Work Environ Health. 2018;44(1):16–24. doi:10.5271/sjweh.3659.
    1. Langer CE, Mitchell DC, Armitage TL, et al. Are Cal/OSHA regulations protecting farmworkers in California from heat-related illness? J Occup Environ Med. 2021; 63(6):532–539. doi:10.1097/JOM.0000000000002189.
    1. Nakata H, Kakigi R, Shibasaki M. Effects of passive heat stress and recovery on human cognitive function: an ERP study. PLoS One. 2021;16(7):e0254769. doi:10.1371/journal.pone.0254769.
    1. Bernard TE, Kenney WL. Rationale for a personal monitor for heat strain. Am Ind Hyg Assoc J. 1994; 55(6):505–514. doi:10.1080/15428119491018772.
    1. Mitchell DC, Castro J, Armitage TL, et al. Recruitment, methods, and descriptive results of a physiologic assessment of Latino farmworkers: The California Heat Illness Prevention Study. J Occup Environ Med. 2017;59(7):649–658. doi:10.1097/JOM.0000000000000988.
    1. Mix JM, Elon L, Mac VV, et al. Physical activity and work activities in Florida agricultural workers. Am J Ind Med. 2019;62(12):1058–1067. doi:10.1002/AJIM.23035.
    1. Quiller G, Krenz J, Ebi K, et al. Heat exposure and productivity in orchards: implications for climate change research. Arch Environ Occup Health. 2017; 72(6):313–316. doi:10.1080/19338244.2017.1288077.
    1. Wilkinson D, Carter J, Richmond V, Blacker S, Rayson M. The effect of cool water ingestion on gastrointestinal pill temperature. Med Sci Sports Exerc. 2008; 40(3):523–528. doi:10.1249/MSS.0B013E31815CC43E.
    1. Buller MJ, Delves SK, Fogarty AL, Veenstra BJ. On the real-time prevention and monitoring of exertional heat illness in military personnel. J Sci Med Sport; Online Ahead of Print. 2021;24(10):975–981.) doi:10.1016/j.jsams.2021.04.008.
    1. Eggenberger P, MacRae B, Kemp S, Bürgisser M, Rossi R, Annaheim S. Prediction of core body temperature based on skin temperature, heat flux, and heart rate under different exercise and clothing conditions in the heat in young adult males. Front Physiol. 2018;9:1780. doi:10.3389/FPHYS.2018.01780.
    1. Niedermann R, Wyss E, Annaheim S, Psikuta A, Davey S, Rossi R. Prediction of human core body temperature using non-invasive measurement methods. Int J Biometeorol. 2014;58(1):7–15. doi:10.1007/S00484-013-0687-2.
    1. Richmond V, Davey S, Griggs K, Havenith G. Prediction of core body temperature from multiple variables. Ann Occup Hyg. 2015;59(9):1168–1178. doi:10.1093/ANNHYG/MEV054.
    1. Yokota M, Berglund L, Cheuvront S, et al. Thermoregulatory model to predict physiological status from ambient environment and heart rate. Comput Biol Med. 2008;38(11–12):1187–1193. doi:10.1016/J.COMPBIOMED.2008.09.003.
    1. Showers K, Hess A, Telfer B. 2016. Validation of core temperature estimation algorithm. Project Report PSM-4. Lexington, MA: Lincoln Laboratory of the Massachusetts Institute of Technology. . Accessed 20 Sept 2021.
    1. Looney DP, Buller MJ, Gribok AV, et al. Estimating resting core temperature using heart rate. J Meas Physical Behaviour. 2018;1(2):79–86. doi:10.1123/jmpb.2017-0003.
    1. Buller MJ, Davey T, Fallowfield JL, Montain SJ, Hoyt RW, Delves SK. Estimated and measured core temperature responses to high-intensity warm weather military training: Implications for exertional heat illness risk assessment. Physiol Meas. 2020;41(6):065011. doi: 10.1088/1361-6579/ab934b.
    1. Spector JT, Krenz J, Calkins M, et al. Associations between heat exposure, vigilance, and balance performance in summer tree fruit harvesters. Appl Ergon. 2018;67:1–8. doi: 10.1016/j.apergo.2017.09.002.
    1. Spector JT, Krenz J, Rauser E, Bonauto DK. Heat-related illness in Washington State agriculture and forestry sectors. Am J Ind Med. 2014;57(8):881–895. doi: 10.1002/ajim.22357.
    1. WA Employment Security Department. 2013. Agricultural workforce in Washington State, . Accessed 20 Sept 2021.
    1. Western Regional Climate Center. 2014. Climate of Washington. . Accessed 20 Sept 2021.
    1. Domitrovich JW, Cuddy JS, Ruby BC. Core-temperature sensor ingestion timing and measurement variability. J Athl Train. 2010;45(6):594–600. doi:10.4085/1062-6050-45.6.594.
    1. Huggins R, Glaviano N, Negishi N, Casa DJ, Hertel J. Comparison of rectal and aural core body temperature thermometry in hyperthermic, exercising individuals: a meta-analysis. J Athl Train. 2012;47(3): 329–338. doi: 10.4085/1062-6050-47.3.09.
    1. Moran D, Shitzer A, Pandolf K. A physiological strain index to evaluate heat stress. Am J Physiol. 1998; 275(l):R129–34. doi:10.1152/ajpregu.1998.275.1.R129.
    1. Spector JT, Krenz J, Blank KN. Risk factors for heat-related illness in Washington crop workers. J Agromedicine. 2015;20(3):349–359. doi:10.1080/1059924X.2015.1047107.
    1. Centers for Disease Control and Prevention. 2014. How is BMI calculated and interpreted? About BMI for adults. Centers for Disease Control and Prevention. . Accessed 20 Sept 2021.
    1. ACGIH. Heat Stress and Strain: TLV® Physical Agents. Cincinnati, OH: American Conference of Governmental Industrial Hygienists; 2015. ISBN 9781-607260-77-6.
    1. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327(8476):307–310. doi:10.1016/S0140-6736(86)90837-8.
    1. R Development Core Team. 2011. R: A language and environment for statistical computing. R Foundation for Statistical Computing, . Accessed 20 Sept 2021.
    1. Datta D 2018. blandr: Bland-Altman Method Comparison, . . Accessed 5 August 2021.
    1. Buller M, Tharion W, Duhamel C, Yokota M. Realtime core body temperature estimation from heart rate for first responders wearing different levels of personal protective equipment. Ergonomics. 2015;58(11): 1830–1841. doi:10.1080/00140139.2015.1036792.
    1. Hunt AP, Buller MJ, Maley MJ, Costello JT, Stewart IB. Validity of a noninvasive estimation of deep body temperature when wearing personal protective equipment during exercise and recovery. Mil Med Res. 2019;6(1):20. doi:10.1186/s40779-019-0208-7.
    1. HQInc. CorTempTM core body temperature monitoring system user manual. Palmetto, FL; 2015. . Accessed 20 Sept 2021.
    1. Lefrant J-Y, Muller L, de La Coussaye JE, et al. Temperature measurement in intensive care patients: comparison of urinary bladder, oesophageal, rectal, axillary, and inguinal methods versus pulmonary artery core method. Intensive Care Med. 2003;29(3): 414–418. doi:10.1007/s00134-002-1619-5.
    1. Bräuer A, Weyland W, Fritz U, Schuhmann MU, Schmidt JH, Braun U. Determination of core body temperature. A comparison of esophageal, bladder, and rectal temperature during postoperative rewarming. Anaesthesist. 1997;46(8):683–688. doi:10.1007/s001010050454.
    1. Mac V, Elon L, Mix J, et al. Risk factors for reaching core body temperature thresholds in Florida agricultural workers. J Occup Environ Med. 2021;63(5): 395–402. doi:10.1097/JOM.0000000000002150.

Source: PubMed

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