Metabolic and Cardiovascular Responses to a Simulated Commute on an E-Bike : Translational Journal of the American College of Sports Medicine

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Metabolic and Cardiovascular Responses to a Simulated Commute on an E-Bike

Alessio, Helaine M.1; Reiman, Timothy1; Kemper, Brett1; von Carlowitz, Winston1; Bailer, A. John2; Timmerman, Kyle L.1

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Translational Journal of the ACSM 6(2):e000155, Spring 2021. | DOI: 10.1249/TJX.0000000000000155
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Electric bicycles (e-bikes) with battery-powered assist may overcome barriers to active transportation (i.e., time and perceived effort), benefit cardiometabolic health, and be an environmentally friendly transportation option. This study aimed to compare cardiometabolic responses and ratings of perceived exertion (RPE) between a regular bike and an e-bike at two different assist levels during a 3-mile ride. During the same day and in randomized order, male (n = 16) and female (n = 14) participants (19–61 yr) completed three outdoor 3-mile bike rides, simulating a typical commute, at their own pace on a regular bicycle and on an e-bike at two different assist levels. Heart rate (HR) and oxygen consumption (V̇O2) were monitored continuously during each ride. RPE was reported at the end of each 3-mile ride. Linear mixed effects models were created to compare different bike conditions while accounting for the repeated responses for each rider. Significant differences existed between riding a regular bicycle compared with an e-bike at assist levels 1 and 2, respectively, for %HRmax (66 vs 62 vs 56), % of V̇O2max (55 vs 47 vs 39), RPE (12.3 vs 9.8 vs 8.4), metabolic equivalents (METs) (6.5 vs 5.6 vs 4.6), caloric expenditure (505 vs 422 vs 344 kcal·h−1), and time (865 vs 748 vs 681 s). The faster times and the lower perceived exertion associated with the e-bike may incentivize active transportation. Further, while the cardiometabolic responses (e.g., HR and V̇O2) were lower for the e-bike, they were indicative of being at or near “moderate intensity,” suggesting that e-bike use may still benefit health-related fitness.


Despite recommendations from the first edition of Physical Activity Guidelines in 2008, Americans of all ages have been spending more hours per day being sedentary and less time being active (1). Even with public health reports promoting regular physical activity, most adults do not achieve recommendations of a weekly accumulation of activity of 150 min of moderate or 75 min of vigorous intensity (2,3). Reasons for failing to meet these physical activity recommendations include more time spent driving cars, increased appeal of engaging in sedentary screen activities, and lack of time for regular physical activity or exercise (4). Given the dismal exercise adherence rate of the majority of adults (3), advocates for physical activity have turned to novel ways to achieve weekly recommendations from the World Health Organization and American College of Sports Medicine (ACSM) (5,6).

An opportunity for increasing physical activity and decreasing sedentary time involves active transportation for commuting or for leisure (7). Rather than attempting to schedule leisure time activity during a busy day, active transportation represents a more organic activity that may include walking, bicycling, wheelchair rolling, and virtually any physical activity contrary to sitting in a chair or vehicle. Federal, state, and local departments of transportation and organizations, including the ACSM’s (8) ActivEarth Task Force, League of American Bicyclists, ecoAmerica, and Climate for Health, promote diverse modes of active transportation to reduce sedentary time. Commuting via some form of active transportation provides an opportunity for persons of all ages to engage in physical activity, even for short periods of time, especially given that the new physical activity guidelines no longer have a minimum time threshold for bouts of physical activity (9). An added benefit is that commuting by bike instead of a gas fueled vehicle reduces greenhouse gas emissions significantly, which benefits the environment (10,11).

In the United States, active commuting in the form of cycling accounts for a mere 0.4% of rural commuters and 0.6% of urban commuters (12). Reasons for not biking include perceptions of safety concerns and of intense fatigue accompanied by excessive sweating and uncertainty in meeting energy requirements associated with riding uphill (7). Electric bicycling in the United States is slowly emerging as a viable transportation alternative that directly addresses some of the main reasons that people do not commute by bike (13). An electric bicycle or e-bike is a modified traditional bicycle incorporating the use of an electric motor to accelerate the bicycle (14). E-bike riders receive assistance as they pedal through a separate crank system on a bicycle powered by a rechargeable battery (14). The bicyclist must still pedal to propel the bike, but with an e-bike, the workload is reduced, depending on the assist level controlled by the bicycle rider. E-bikes enable longer distances and reduce barriers related to low perceived or actual fitness level, hills, and wind conditions (15). In 2015, 140,000 e-bikes were sold in the United States (16). In the same timeframe, Europe and China sold 1.2 million and ~30 million electric bikes, respectively (16). Individuals who purchased e-bikes increased the distance they were able to cycle as well as the usage of this mode of active transportation compared with other transportation options (17).

Although work intensity is reduced in proportion to the assist level on an e-bike, Höchsmann et al. (18) reported in a randomized study of overweight adults that the potential for e-bike riders to improve cardiorespiratory fitness was similar to regular bicyclists. After a month, differences in peak oxygen consumption, blood pressure, body composition, and maximal ergometric workload were within 2% for e-bike and regular bicycle riders, with trends for improvement that were similar. Van Cauwenberg et al. (19) found that e-bikes appealed to adults at risk for physical inactivity because of their physical activity patterns, age, and body composition. This was despite the fact that it was also reported that 27% of the volunteers had experienced an e-bike crash, most frequently due to an uneven or slippery surface. If e-bikes can increase bouts of physical activity intense enough to increase some health parameters, but not so intense that they are perceived negatively, then e-bike use may be an effective, novel way to increase energy expenditure and improve fitness, especially in populations that tend to be sedentary.

The purpose of this study was to compare the cardiometabolic responses (e.g., heart rate [HR] and oxygen consumption [V̇O2]), perceived effort, and attitudes of adults when riding a regular bicycle and an e-bike at two different assist levels for 3 miles. We hypothesized that energy expenditure and cardiovascular response to a 3-mile e-bike ride, which approximates an average bicycle commute (20), would be lower than a regular bicycle, yet meet guidelines for health benefits associated with physical activity. We also hypothesized that perceived effort and attitudes toward biking for 3 miles when riding an e-bike would be more favorable compared with a regular bike, which is apt to lead to more riding and accumulated physical activity.


Volunteers were recruited using flyers and informational sessions presented at a variety of settings (e.g., local senior center, recreation center, and gymnasiums). Interested volunteers were provided with a description of the study and a written informed consent statement that was approved by Miami University’s Institutional Review Board. Eligibility to participate included the following: free of any acute injury or clinically significant disease, age between 18 and 68 yr, and willingness to ride a regular and an e-bike. Thirty volunteers (n = 16 males, n = 14 females) were included in this study. Most participants were university students (age, 18–22 yr) who were relatively fit.

Sample Size

Sample size was determined based on data from a similarly designed e-bike study (13). They reported that intensity in metabolic equivalents (METs) was 6.1 ± 1.6 METs during a “no assist” versus 5.7 ± 1.2 METs during a “light assist” exercise trial on an e-bike. Based on these data, we estimated that a minimum of 16 subjects would provide 80% power to detect significant intensity differences between our trials.

Preliminary Measurements

Each volunteer completed standard body weight, height, and resting blood pressure measurements. They also completed the Physical Activity Readiness Questionnaire (21) and the International Physical Activity Questionnaire (22). Each volunteer performed a YMCA submaximal cycle ergometer test to predict V̇O2peak (23). This test was performed on a stationary cycle ergometer and lasted between 9 and 12 min, depending on HR response. HR (Polar T31 HR monitor) and oxygen consumption (V̇O2) (portable, indirect calorimeter; Cosmed K5, Rome, Italy) were continuously monitored during the submaximal test. No test was terminated for clinical reasons. Descriptive data for the participants are shown in Table 1.

TABLE 1 - Demographic Descriptions of Participants.
Variable Mean SD Median
Age (yr) 26.2 12.7 21.0
Height (m) 1.8 0.1 1.8
Weight (kg) 77.6 18.4 71.7
Body mass index (kg·m−1) 25.1 4.2 23.9
V˙O2max (mL·kg−1·min−1) 42.6 9.3 39.8
Systolic blood pressure (mm Hg) 122.2 15.5 123.0
Diastolic blood pressure (mm Hg) 73.8 11.9 74.0
Mean arterial pressure (mm Hg) 87.7 10.4 88.0
Resting HR (b·min−1) 77.9 17.0 75.0
MET weekly activity (MET·min·wk−1) 4505 2870 4159
Data are presented as mean, SD, and median for all participants (N = 30).

Testing Conditions

E-bike and regular bicycle riding were performed outdoors on a 0.5-mile flat concrete oval path. The rides took place between August and October and between April and June. Weather conditions were dry with temperatures ranging between 16°C and 27°C. Participants were fitted to the bicycles according to their height and the seat was set so that a knee angle in a range of 20°–30° occurred on the vertical down pedal stroke. Each participant completed a total of three 3-mile rides using the regular bike and an e-bike under a low assist level (E-1) and an e-bike under a moderate assist level (E-2) with the order of the three rides randomized. All participants wore helmets while biking.

Equipment Used

The regular bicycle was an ordinary touring bicycle (WTB Iron Horse MT 300 with derailleur gears (21 speed), Pacific Cycle Inc., Olney, IL, USA) weighing 13.4 kg. Participants selected the gear that felt most comfortable for their ride. The e-bike (2017 iZip Model E3 Vibe Plus, Simi Valley, CA) was a class 1 pedal electric with 20 mph top speed. Power was provided by a TransX mid drive motor with 350 W connected to a 48-V lithium ion battery. The e-bike weighed 24.5 kg, with 7.2 kg attributed to the electric motor and battery. The motor provided power to the bicycle crank during pedaling, which assisted the rider up to a speed of 20 mph, a state requirement to be considered a bicycle. Assist E-1 resulted in range of 125–175 W assistance to the rear wheel and assist E-2 resulted in a range of 200–250 W assistance, depending on the gear (total of seven) and pedal rate. For all three rides, participants were instructed to ride at a comfortable pace that would simulate a commute to work or school. As with preliminary exercise testing, V̇O2 and HR were measured continuously. The Borg RPE scale was used to monitor each participant’s perception of how they felt before and after the regular bike ride and e-bike ride. Participants also completed a questionnaire with 18 questions (Appendix A addressing their current perception and likelihood of riding an e-bike in the future.

Statistical Analyses

Linear mixed effect models (24) were used to analyze the effects of bicycle type and sequence on metabolism, HR, and perceived effort when riding a regular bicycle and an e-bike at two different assist levels, for 3 miles per bicycle while potentially controlling for other predictor variables. Effect sizes were constructed as confidence intervals between the responses between one biking condition (e.g., e-bike at E-1) and another biking condition (e.g., regular bike). RStudio (25) was used for data cleaning, comparison plots, calculation of mean and variance, and creating a sequence variable (26,27). The SAS MIXED procedure was used to fit the linear mixed effect model, and a Bonferroni adjustment was used to adjust for multiple comparisons.

Attitudes before and after riding both regular and e-bikes were compared by analyzing the frequency of responses to each of the 18 questions. Confidence intervals for differences in proportion and frequency tables were created using the SAS FREQ procedure.



Thirty participants were included in this study. Table 1 contains descriptive data for body composition and cardiometabolic variables for all participants.

Differences were observed when comparing HR, V̇O2, and perceptions when riding an e-bike at either assist level (E-1 or E-2) with a regular bicycle. Figs. 1–4 show the distribution of total caloric energy expenditure, percent of V̇O2max, percent of HRmax, and RPE when participants rode an e-bike at assist level E-1, assist level E-2, and a regular bicycle for 3 miles each. Note that each participant contributed to the distribution of each biking condition. A stepwise change in energy expenditure, oxygen consumption, HR response, and perceived effort occurred with the regular bicycle riding having the highest energy, HR, and perceived effort; e-bike at assist level E-1 having lower values for each than the regular bicycle; and e-bike at assist level E-2 having the lowest intensity of all. No significant differences occurred in metabolic, cardiovascular, or perceived effort when comparing e-bikes ridden at either assist level. An important observation was that even when the effort was perceived as lower for the e-bikes, exercise intensity fell within or near moderate intensity for E-1 and E-2. Percent HRmax mean for E-1 was 62% with a range of 33%–88%, and %V̇O2max mean was 47% with a range of 12–88%. For E-2, the mean for %HRmax was 56%, with a range of 23%–79%, and mean %V̇O2max was 39% with a range of 17%–80%. Moderate intensity is the general recommendation for exercise intensity by the ACSM (8), which considers 64%–76% HRmax and 46%–63% of V̇O2max, moderate intensity. MET levels for all bikes, regular and e-bike regardless of assist level, fell within moderate MET levels of 3–5.9 METs, considered by ACSM to be moderate intensity. Collectively, these results indicated that when compared with a regular bike, riding an e-bike at both levels of assistance resulted in intensities at or near moderate intensity physical activity.

Figure 1:
Box plots comparing hourly energy expenditure for different bike riding conditions. Data presented as median for each group (box) with data points outside the standard error of the mean displayed.
Figure 2:
Box plots comparing percent of V̇O2max for different bike riding conditions. Data presented as median for each group (box) with data points outside the standard error of the mean displayed.
Figure 3:
Box plots comparing percentage of HRmax for different bike riding conditions. Data presented as median for each group (box) with data points outside the standard error of the mean displayed.
Figure 4:
Box plots comparing RPE (Borg RPE scale) for different bike riding conditions. Data presented as median for each group (box) with data points outside the standard error of the mean displayed.

Based on differences in the Borg RPE scale (Fig. 4), riding a regular bike had a mean ± SD RPE score of 12.3 ± 2, which translated to a perception of “light to somewhat hard”; riding an e-bike at E-1 had an RPE of 9.8 ± 2.2 equivalent to a perception of “very light to light”; and riding an e-bike at assist level E-2 had an RPE of 8.4 ± 2.5, translating to “extremely light to very light.”

The mean ± SD total time taken to ride 3 miles was 822 ± 133 s on a regular bike, 707 ± 69 s for an e-bike at E-1, and 621 ± 104 s for E-2. Riding a regular bike for 3 miles on average took 24% longer than riding an e-bike at either assist level, translating to a 2.5- to 3.9-min faster 3-mile travel time. Mean ± SD MET levels for the regular bicycle and E-1 and E-2 assist levels were 6.7 ± 2.0, 5.8 ± 2.6, and 4.8 ± 2.1, respectively. Riding an e-bike at E-2 took less time at a lower MET level than riding an e-bike at E-1, which took less time at a lower MET level than riding a regular bike. Riding a regular bike took the longest time and had the highest MET level of all bikes.

Generally, most participants had either a positive or no change in attitude after e-bike use. Only two participants had a negative response to riding an e-bike. In a simple descriptive summary of text results, either survey (before or after riding an e-bike), the biggest concern was a perception of reduced control. After riding an e-bike, the two most popular reasons stated for likelihood of riding an e-bike in the future were “easier” and “commute” illustrated in the word cloud where the size of the words displayed is directly linked to the frequency of the words written in subjective responses by all of the participants (Fig. 5).

Figure 5:
Word cloud illustration of most frequently stated e-bike benefits as described in writing by participants.


In the present study, average MET levels during a 3-mile ride for e-bike assist level E-2 was 4.8, which was lower than the e-bike assist at level E-1 (5.8 METs) and lower still than the regular bicycle’s MET level (6.7 METs). All three MET levels, if performed regularly, meet the minimum intensity for healthy physical activity (28). The ACSM categorizes activities as <3 METs = light, 3–5.9 METs = moderate, and >6 = vigorous (8). In this case, the MET values for both e-bike assist levels fall within moderate intensity. Similarly, mean %HR max for E-2 (56%) and E-1 (62%) approach the minimal target HR range of 64%–76% of HR max representing moderate intensity. Regardless of the type of bicycle used to travel a 3-mile distance, the result was an elevated metabolic and cardiovascular response, which if performed regularly would likely lead to the improvement or maintenance of cardiovascular health and caloric balance (18).

Despite potential health benefits and positive effect on the environment due to lower greenhouse gas emissions and congestion associated with driving gas-powered vehicles, a very small percentage (3.4%) of Americans bike to work or as a leisure activity (3). In the United States, electric bicycles are a relatively novel form of active transportation that may appeal to some people who might consider a commute by riding a bicycle if not for perceived limitations that may include time, fatigue, and safety (29). This active transportation option could eventually benefit the environment because reduced automobile use would result in less congestion and pollution (5,30). Furthermore, active transportation reduces sedentary time and increases the potential for health benefits in individuals due to elevated metabolic and HR response, if used regularly (31). When combined, a less polluted environment and more physical activity from active transportation can positively affect health (32,33).

Personal health benefits of riding e-bikes may include increased caloric expenditure for weight management and increased metabolic and cardiovascular activity that could enhance health-related fitness (18). When riding on the highest assist level, it was reported that the effort on the e-bike was comparable with the effort walking up a 6.5% grade at a self-selected pace on a treadmill, which is equivalent to a moderate to vigorous intensity physical activity (5). In the present study, the initial concerns expressed by the participants in the surveys about control and safety implied that riding an e-bike at the highest assist level was not reasonable or safe. In this study, the two assist levels were chosen to meet anticipated safety concerns of participants, especially those who were new to e-bikes, concerns likely to generalize to many others considering e-bike commuting as an active transportation option.

La Salle et al. (2017) reported that electric bicycles reduced the amount of time to complete a commute as well as decreased an individual’s RPE in comparison with a traditional bike but did not significantly change either oxygen uptake or HR (34). This lack of difference in cardiometabolic responses on an e-bike compared with a regular bike is in contrast to the data reported in the present study. Possible explanations for this discrepancy may include the relatively small sample size in the La Salle et al. study (n = 6) and the use of a real-world course in an urban setting for the bike rides. Despite the differences in cardiometabolic responses between the two studies, both the La Salle et al. study and the current study reported a lower perceived exertion and shorter time when riding an e-bike compared with a regular bike. These results imply that riding an e-bike could accomplish a task quickly and at a reduced perceived exertion.

Analyses of pre- to postsurvey questions about attitudes toward riding an e-bike resulted in the conclusion that the majority of participants experienced a positive change in attitudes toward e-bikes. Major concerns regarding e-bikes included control and navigation of pedestrians. After riding an e-bike, most participants noted the advantages in terms of commute time and ease.

The physiological and attitude data analyzed in this study provide quantitative and qualitative evidence supporting the benefits of e-bikes for both personal health from increasing bouts of physical activity during a typical work day, and potential environmental impact due to less congestion and lowering one’s carbon footprint when an e-bike is used instead of an automobile. HR and caloric expenditure were both increased significantly above resting levels while riding an e-bike. Although these values were lower than the relative heart and metabolic rate of a regular bicycle, the responses during a simulated 3-mile commute on an e-bike reached “moderate intensity” based on %V̇O2max and MET values. As 150 min·wk−1 of moderate intensity aerobic exercise is the recommendation of the ACSM (8), this suggests that e-bike commuting could be part of one’s strategy for accruing the recommended amount of PA for health.

After riding an e-bike, nearly all participants noted a positive attitude that would be likely to motivate them to use one, if available. This would contribute to physical activity as part of an active commute instead of a planned, structured activity, which according to exercise adherence research is difficult to sustain. One concern expressed by some volunteers in this study was the risk of an accident on an e-bike. This was communicated in the survey by the use of words such as “control” as a barrier. Furthermore, accidents on bicycles of any type, regular or e-bikes, are bound to occur as previously reported (19). A recent report indicated that bicycle accidents are on the rise in many cities (35), and this must be addressed in many ways: bike safety knowledge for both car drivers and bike riders; safe streets, including dedicated bike paths; helmet laws; and policies that encourage safety for all who travel on roads, regardless if walking or riding in a vehicle.

It is important to note that this study was conducted with a predominantly college-age group of riders on a relatively flat track. Future studies would benefit from a comparison of traditional and e-bikes in older riders on a course with more hills.


In conclusion, compared with a regular bicycle, riding an e-bike for a simulated 3-mile commute resulted in lower metabolic, cardiovascular, and perceived effort that nevertheless met the intensity level associated with healthy physical activity recommended by the World Health Organization and the ACSM. Positive perceptions toward e-bike riding occurred in most participants, and qualitative analyses included perceptions of commuting with an e-bike as “easier” and “fun,” among other positive terms. Together, these results imply that e-bikes are viable options for active transportation that can benefit individual health and reduce congestion and pollution from gas-powered vehicles.

Students in STA 660, a data practicum class at Miami University, implemented these models, Rachel Hopkins, Miami University Statistical Consulting Center (SCC), created the graphics, and Mike Hughes (SCC) provided additional support for the data analysis. Courtney Kemper, Ryan Mullen, McKenna DiRe, and Arden McMath assisted in data collection.

The authors have no professional relationships with companies or manufacturers who will benefit from the results of the present study. Two of the authors frequently use e-bikes for commuting to work and errands.

The results of the present study do not constitute endorsement by the ACSM.


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