JMIR mHealth and uHealth (Jul 2020)

Usability of Wearable Devices With a Novel Cardiac Force Index for Estimating the Dynamic Cardiac Function: Observational Study

  • Hsiao, Po-Jen,
  • Chiu, Chih-Chien,
  • Lin, Ke-Hsin,
  • Hu, Fu-Kang,
  • Tsai, Pei-Jan,
  • Wu, Chun-Ting,
  • Pang, Yuan-Kai,
  • Lin, Yu,
  • Kuo, Ming-Hao,
  • Chen, Kang-Hua,
  • Wu, Yi-Syuan,
  • Wu, Hao-Yi,
  • Chang, Ya-Ting,
  • Chang, Yu-Tien,
  • Cheng, Chia-Shiang,
  • Chuu, Chih-Pin,
  • Lin, Fu-Huang,
  • Chang, Chi-Wen,
  • Li, Yuan-Kuei,
  • Chan, Jenq-Shyong,
  • Chu, Chi-Ming

Journal volume & issue
Vol. 8, no. 7
p. e15331


Read online

BackgroundLong-distance running can be a form of stress to the heart. Technological improvements combined with the public’s gradual turn toward mobile health (mHealth), self-health, and exercise effectiveness have resulted in the widespread use of wearable exercise products. The monitoring of dynamic cardiac function changes during running and running performance should be further studied. ObjectiveWe investigated the relationship between dynamic cardiac function changes and finish time for 3000-meter runs. Using a wearable device based on a novel cardiac force index (CFI), we explored potential correlations among 3000-meter runners with stronger and weaker cardiac functions during running. MethodsThis study used the American product BioHarness 3.0 (Zephyr Technology Corporation), which can measure basic physiological parameters including heart rate, respiratory rate, temperature, maximum oxygen consumption, and activity. We investigated the correlations among new physiological parameters, including CFI = weight * activity / heart rate, cardiac force ratio (CFR) = CFI of running / CFI of walking, and finish times for 3000-meter runs. ResultsThe results showed that waist circumference, smoking, and 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 were controlled, contestants with a larger waist circumference tended to fail the qualification based on the formula above. If waist circumference and ACi7 were controlled, smokers tended to fail more often than nonsmokers. Finally, we investigated a new calculation method for monitoring cardiac status during exercise that uses the CFI of walking for the runner as a reference to obtain the ratio between the cardiac force of exercise and that of walking (CFR) to provide a standard for determining if the heart is capable of exercise. A relationship is documented between the CFR and the performance of 3000-meter runs in a healthy 22-year-old person. During the running period, data are obtained while participant slowly runs 3000 meters, and the relationship between the CFR and time is plotted. The runner’s CFR varies with changes in activity. Since the runner’s acceleration increases, the CFR quickly increases to an explosive peak, indicating the runner’s explosive power. At this period, the CFI revealed a 3-fold increase (CFR=3) in a strong heart. After a time lapse, the CFR is approximately 2.5 during an endurance period until finishing the 3000-meter run. Similar correlation is found in a runner with a weak heart, with the CFR at the beginning period being 4 and approximately 2.5 thereafter. ConclusionsIn conclusion, the study results suggested that measuring the real-time CFR changes could be used in a prediction model for 3000-meter running performance.