Article Text
Abstract
Objective Exertional heat stroke (EHS), characterised by a high core body temperature (Tcr) and central nervous system (CNS) dysfunction, is a concern for athletes, workers and military personnel who must train and perform in hot environments. The objective of this study was to determine whether algorithms that estimate Tcr from heart rate and gait instability from a trunk-worn sensor system can forward predict EHS onset.
Methods Heart rate and three-axis accelerometry data were collected from chest-worn sensors from 1806 US military personnel participating in timed 4/5-mile runs, and loaded marches of 7 and 12 miles; in total, 3422 high EHS-risk training datasets were available for analysis. Six soldiers were diagnosed with heat stroke and all had rectal temperatures of >41°C when first measured and were exhibiting CNS dysfunction. Estimated core temperature (ECTemp) was computed from sequential measures of heart rate. Gait instability was computed from three-axis accelerometry using features of pattern dispersion and autocorrelation.
Results The six soldiers who experienced heat stroke were among the hottest compared with the other soldiers in the respective training events with ECTemps ranging from 39.2°C to 40.8°C. Combining ECTemp and gait instability measures successfully identified all six EHS casualties at least 3.5 min in advance of collapse while falsely identifying 6.1% (209 total false positives) examples where exertional heat illness symptoms were neither observed nor reported. No false-negative cases were noted.
Conclusion The combination of two algorithms that estimate Tcr and ataxic gate appears promising for real-time alerting of impending EHS.
- hot temperature
- heat-shock response
- physical activity
- preventive medicine
Data availability statement
No data are available. Data are not publicly available.
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Data availability statement
No data are available. Data are not publicly available.
Footnotes
Contributors Study conception and design: MBul, RF, JS, MG and MBur; significant data acquisition: MBul, MBur, MG, BAB, MJM, KD, TM, CK, RF and CM; significant data analysis: MBul, EA, MJM, KD, TM, LW, BT and JRW; significant writing: MBul, RF, EA and JRW; critical review and revision input: all listed authors.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Disclaimer The views expressed in this paper are those of the authors and do not reflect the official policy of the Department of Army or Department of Defence (DoD). The MIT Lincoln Laboratory contribution to this work was supported by the Department of the Army under Air Force Contract No. FA8702-15-D-0001.
Competing interests MBul is the listed inventor on a patent for estimating core body temperature from heart rate, also known as estimated core temperature (ECTemp). Ownership of the ECTemp patent is held by the US Department of the Army. MBul, RF, JS, BT and JRW and LW are the listed inventors on a patent application, 'System and Method to Predict Exertional Heat Stroke from Torso-Worn Sensor'. This patent is jointly owned by the US Department of the Army and Massachusetts Institute of Technology, Lincoln Laboratory. As part of the Department of Defense employee incentive programmes, MBul, RF and JS are entitled to receive a portion of royalty payments for any licensing of the algorithms.
Provenance and peer review Not commissioned; externally peer reviewed.
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