JMIR Publications

ABSTRACT

Background:

Regular physical exercise has many health benefits, but heavy and strenuous exercise may have a negative impact on cardiac function. Marathons and long-distance running can be a form of stress to the heart. Technological improvements combined with the public’s gradual turn towards mHealth, self-health, and exercise effectiveness has resulted in the widespread use of wearable exercise products. The monitoring of dynamic cardiac function changes during running and running performance can be further studied.

Objective:

We investigated the relationship between the dynamic cardiac function changes and the finish time for 3000-meter runs. Using a wearable device based on a novel cardiac force index (CFI), we also tried to explore the potential correlations among 3000-meter runners with stronger and weaker cardiac functions during running.

Methods:

This study used the American Zephyr™ product BioHarness 3.0. This item can measure basic physiological parameters, including the heart rate, respiratory rate, temperature, maximum oxygen consumption, and activity. We investigated the correlations among new physiological parameters, including the CFI = weight * activity / heart rate, cardiac force ratio (CFR) = CFI of running / CFI of walking, and finish times for 3000-meter runs.

Results:

According to an analysis using a multivariate logistic regression model, the accumulation of heart activities during running was statistically significant for qualifying in the 3000-meter running test. The results showed that waist circumference, smoking, and the CFI were the significant factors for qualifying in the 3000-meter run. The prediction model was as follows: ln (3000 meters running performance pass probability / fail results probability) = - 2.702 - 0.096 × [waist circumference] - 1.827 × [smoke] + 0.020 × [ACi7]. If smoking and the ACi7 are controlled, contestants with a larger waist circumference tend to fail the qualification based on the formula above. If the waist circumference and ACi7 are controlled, smokers tend to fail more often than nonsmokers. There was no significant difference between the estimated time and the actual completion time (p = 0.424), indicating no significant difference between the estimated completion time and the actual measurement of the number of seconds in this prediction model. Finally, we compared the CFR correlations between runners with stronger and weaker cardiac force during the 3000-meter run. This study investigates a new calculation method for monitoring the cardiac status during exercise, which uses the CFI of walking for the runner as a reference to obtain the ratio between the CF of exercise and that of walking (CFR) to provide a standard for determining if the heart is capable of the exercise. A relationship is documented between the CFR of a healthy 22-year-old person with strong cardio force. In this example, a representative runner has a strong heart. During the running period, data are obtained while the user slowly runs 3000 meters. The post-running period is the time after the runner has stopped running. The relationship between the CFR and the time is plotted. Thus, this finding shows the runner’s CFR varying with changes in activity. Since the runner’s acceleration increases, the CFR quickly increases to an explosive peak, indicating the runner’s explosive power. This period shows a CFR of approximately 3. After the explosive peak, the CFR begins to decrease to “moderate”, and, after a time lapse, it becomes “gentle”, entering an endurance period at approximately 2.5 CFR. Another relationship is recognized, this time between the CFR of a 44-year-old with weak cardio force. The protocol for this runner is the same as that for the 22-year-old runner’s walking period, running period, and postrunning period. During the running period, data are obtained as the user slowly runs 3000 meters. Again, the runner’s condition changes from relatively static when walking to dynamic when running, and the CFR reaches an explosive peak with acceleration, at approximately 4. After the explosive peak, the CFR decreases sharply and eventually becomes “various”, entering an endurance period at approximately 2.5 CFR.

Conclusions:

In conclusion, the study results suggested that measuring the real-time CFR changes could be used in a prediction model for 3000-meter running performance.