Trial of Sportswear Type ECG Sensor Device for Cardiac Safety Management during Marathon Running

Cardiac arrest has been reported during participation in several sports. Of these sports, marathon running is a particularly popular sport but imposes high cardiac load. Indeed, its popularity has been growing worldwide. Risk of cardiac arrest during marathon races is also expected to increase. Several studies have recorded electrocardiographic (ECG) information during marathon races to protect athletes from cardiac arrest. Although evaluable ECG data have been obtained and analyzed, cost-effectiveness of the system, data quality, and clinical significance remain inadequate. This report is the first to describe an economical electrocardiograph built into a T-shirt for use during marathon race. Twenty healthy runners aged 20 to 59 years (mean 36 years) wore the ECG device while running. The ECG data were monitored and analyzed to assess the observed frequencies of specified arrhythmias and the sections of the marathon in which the arrhythmias occurred. Of the ECG data obtained from 14 runners who completed the full marathon, six ECG datasets were evaluable. In some runners, there was inadequate contact between the electrode and body surface or poor Bluetooth connection between the ECG wireless transmitter and smartphone. Regarding arrhythmia analysis, all evaluable data that were analyzed showed some rhythm fluctuations. In conclusion, this economical T-shirt type ECG sensor provided evaluable ECG data during marathon races, although the evaluable rate was not high. The data were used to analyze specified arrhythmias, but some difficulties were encountered. The ECG sensor did not function properly because of a system error. The ECG sensor was not adequately moistened to record ECGs accurately. Moreover, some runners chose an unsuitable shirt size, which impaired the stability and strength of the electrode–skin contact. These shortcomings produced noise in the ECG data, which made it difficult to analyze arrhythmias. The next step will be to solve these problems and acquire data from a large number of runners.