Integrated Physiological, Biomechanical, and Subjective Responses for the Selection of Assistive Level in Pedelec Cycling - Yang, Sheng-Chieh; Lee, Yun-Ju (Frontiers in Physiology, 2021)
In recent decade, pedelec has become one of the most popular transportation modes due to its effectiveness in reducing physical effort. The effects of using pedelec as an alternative mode of exercise were explored in previous studies. However, the effects of pedelec parameters were not quantified for the self-selected gear ratio, random riding speed, and varied road slopes, which restricted its application. Hence, this study aimed to evaluate the effects of gear ratio and assistive torque and to determine the optimum riding condition regarding physiological, biomechanical, and subjective responses of the rider. The riding tests consisted of simulated slope (1.0 vs. 2.5% grade), gear ratio (light vs. heavy), and assistive levels (0.5, 1, 1.5, and 2), and the tests were conducted in a randomized order. A total of 19 non-athletes completed the riding tests to evaluate physiological [metabolic equivalent of task (MET), heart rate, and gross efficiency (GE)], biomechanical [muscle activity (expressed as reference voluntary contraction, RVC) and power output], and subjective responses [rating of perceived exertion (RPE) and sense of comfort (SC)]. The test conditions induced moderate to vigorous intensities (3.7–7.4 METs, 58.5–80.3% of maximal heart rate, 11.1–29.5% of RVC rectus femoris activity, and 9.4–14.2 RPEs). The effects of gear ratio and assistive level on the physiological responses were significant. Riding with the heavy gear ratio showed advantages in METs and GE. For the optimum assistive level selection, low GE and limited improvement in subjective responses suggested the impact of low-power output conditions. Overall, for the health pedelec commuters, riding with 0.75 W/kg power output with 50 rpm cadence is recommended to obtain the moderate intensity (4.7 METs) and the advantages in GE and subjective feelings. Moreover, the findings can be applied to exercise intensity control and save battery energy effectively in varying riding conditions.