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Published on: October 7, 2015
Agreement between an ingestible telemetric sensor system and a mercury thermometer before and after linear regression
1Human Performance Lab, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.
Objectives:
To evaluate the use of the CorTemp ingestible sensor system to monitor athletes in the prevention of thermal injuries, 3 objectives were established: (1) to determine the agreement between the system and a mercury thermometer and quantify the effect of exceeding the recommended yearly manufacturer calibration, (2) to establish the effect of individual sensor correction on agreement, and (3) to determine the quantity of data required for effective correction.
Design:
Validation study.
Participants:
Ninety-four ingestible sensors.
Interventions:
(1) Five comparisons were made between each sensor and a mercury thermometer across the range of 33 to 41 degrees C. This was performed immediately, and 14 to 18 months, after factory calibration. (2) Linear regression equations were created and used to correct sensor readings at approximately 37 degrees C; the corrected value was compared with the mercury thermometer. (3) Linear regression equations were created for each sensor using 2 to 5 data points.
Main Outcome Measures:
Systematic bias and random error (95%).
Results:
(1) Systematic bias + or - random error (95%) was 0.73% + or - 0.23% ( approximately 0.27 + or - 0.09 degrees C) and 0.54% + or - 0.28% ( approximately 0.22 + or - 0.11 degrees C) immediately and 14 to 18 months post factory calibration, respectively. (2) Regression correction improved agreement through reductions in systematic bias. (3) Individualized equations using a minimum of 3 comparisons were required to reduce agreement to + or - 0.10 degrees C; the use of 5 comparisons minimized the number of readings exceeding + or - 0.10 degrees C.
Conclusions:
(1) The CorTemp system inflates temperature measurements compared with a mercury thermometer. (2) Individual sensor calibration is warranted. (3) Correction equations should use a minimum of 3, preferably 5, comparisons. After regression correction, the system displays satisfactory accuracy for preventative monitoring.
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