Gait instability and estimated core temperature predict exertional heat stroke

Abstract

ObjectiveExertional 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.MethodsHeart 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.ResultsThe 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.ConclusionThe combination of two algorithms that estimate Tcr and ataxic gate appears promising for real-time alerting of impending EHS.

Wearable and ingestible technology to evaluate and prevent exertional heat illness: A narrative review

“…Only two articles were identified that examined the ability of a tracker to predict or identify those with EHS (see Table 3), specifically with wearable technology. Although one of the devices, in conjunction with a gait instability accelerometer, was able to identify EHS casualties 3.5 minutes prior to a collapse, 34 the other found an extremely low sensitivity measure of 20% 35 . More data are needed before these devices can be relied on in the prediction and diagnosis of real‐world heat illness.…”

“…Buller et al demonstrated that estimated core temperature used in conjunction with gait instability can increase sensitivity and specificity for EHI detection. 34 As wearable technology becomes more popular and continues to advance, it is imperative to conduct research and evaluate the utility of this technology in the setting of EHI. Additionally, as some systems have global positioning system (GPS) enabled tracking and body position tracking, flagging individuals who are in a horizontal position for a period of time may be helpful to military members or laborers who are not in direct eye sight of medical or support staff.…”

Exertional heat stroke: an evidence based approach to clinical assessment and management

“…Technologies are currently being tested that may allow real‐time physiological monitoring of participants and for the identification of a EHS casualty prior to collapse (Buller et al., 2021). Using a combination of tri‐axial accelerometry and core temperature prediction of heart rate and skin temperature, retrospective analysis allowed for the detection of EHS casualties 10–15 min prior to collapse (Buller et al., 2022). In the future, it may be possible for an individual participant to self‐monitor their heat status using these technologies.…”

“…Using a combination of tri-axial accelerometry and core temperature prediction of heart rate and skin temperature, retrospective analysis allowed for the detection of EHS casualties 10-15 min prior to collapse (Buller et al, 2022). In the future, it may be possible for an individual participant to self-monitor their heat status using these technologies.…”

Using Body-worn Accelerometers to Detect Physiological Changes During Periods of Blast Overpressure Exposure

“…Accelerometry data was segmented into time periods with gait and low-movement (LM) to distinguish changes over time in head movements that occur during ambulation from those that occur during periods of standing or sitting. Similar methods of activity-based segmentation have been used previously to measure movement abnormalities due to Parkinson's disease [19] and exertional heat stroke [20]. Primary considerations for Gait and LM segmentation are built upon methods established in this prior work.…”