A push for public health: the effect of e-bikes on physical activity levels

In Norway, the annual cycling share is approximately 5 % (with a seasonal variation from one to 8%) [23]. Increasing cycling as a mode of transport is a political goal [3]. In part due to the potential for increased overall physical activity and reduced sedentarism. There is strong evidence for the positive health effects of physical activity, in terms of overall reduced mortality and potential avoidance, or delay, in outbreaks of lifestyle-induced disease (e.g. cardiovascular disease, stroke, colon cancer, breast cancer and type II diabetes) [9]. The health benefits of physical activity (PA) depends on an individual’s baseline fitness (i.e. weight, maximal O₂ uptake) and the frequency, duration and intensity of the activity performed [4, 17]. The Norwegian Directorate of Health recommends that adults engage in at least 150 min of moderate-intensity physical activity per week [41], accumulated through activity bouts of at least 10 min [47]. The intensity of physical activity is indicated by the metabolic equivalent of the task (MET),¹ with the minimum goal in the range 500–1000 MET min per week [21]. Although vigorous physical activity can produce highest level of fitness, the greatest improvements in health are obtained by people who progress from sedentary to moderately active [46]. This means that, a moderate level of activity is most relevant from a public health point of view.

Previous studies have found that substantial physical activity can be accumulated through active travel [10, 34]. E.g. cycling at >15 km/h on a regular bike induces sufficient effort to adequately stimulate fitness [38] and is categorized as moderate activity (MET value of 4) [1]. In line with these estimations, someone changing from a passive transport mode (i.g. car) to bicycle for a total round-trip distance of 7.5 km a day (about 30 min overall, 15 min per trip) would meet the minimum recommendation for physical activity. Hence, increasing the amount of active travel is key for reaching targets for improved public health. It should be noted that an important additional way in which active travel enhances health is by reducing sedentarism. A lifestyle characterised by prolonged time spent in uninterrupted sedentary behaviours (e.g. office-based work and TV watching) can provide health risks independently regardless of engagement in physical exercise [13, 30]. Most active travellers engage in brief trips (10–15 min) twice a day [33], providing important “breaks” in a daily routines characterized by prolonged sedentarism.

In Europe, the sales of e-bikes with an integrated battery assisting the rider’s own pedal-power are increasing [7]. According to European Union regulations, e-bikes are classified legally as bicycles if they fulfil certain criteria, including assistance up to a maximum speed of 25 km/h. In European countries such as Germany, the Netherlands and Switzerland, e-bikes now comprise between 15 and 20% of annual sales [7]. In Norway, e-bikes represent only about 1% of annual bicycle sales [7], but the numbers are increasing.

It has been suggested that e-bikes might result in less physical activity than regular bicycles [24]. However, research has indicated that even though they provide decreased physical activity compared to traditional bikes, they still achieve a level necessary for health enhancement [12, 18]. E-bikes can in fact help overcome perceived barriers to cycling, such as hills or too long distance to destination, yet making the cyclist doing a considerable effort by actively pedalling. Another important aspect is that these benefits might be appealing for certain journeys and for certain people, hence recruiting “new” cyclists. A study in the Netherlands has concluded that giving rewards for commuting by e-bike yields positive results regarding the mobility and health of commuters [43], but more studies are needed if we are to be more informed about the actual effects [12].

It has been suggested that the assumed health effect of increased cycling can be cancelled out by a compensatory reduction in other physical activities (a substitution effect). However, the empirical evidence for substitution is weak [42]. Moreover, fitness gains from increased cycling could inspire individuals to be more active in other domains, hence increasing overall physical activity. Previous studies in adults, would indicate that interventions targeting one specific health behaviour can have a motivational impact on others [29]. Experiencing improved physical fitness and a sense of achievement could in themselves be important factors in increasing and maintaining physical activity [39].

Previous studies in Norway, show that the e-bike is of little interest to people who already cycle a lot for transport or for exercise [16] – and that the e-bike thus serves other purposes and markets than the ordinary bike.

It has been suggested that in countries with low cycling levels, such as Norway, there is a large proportion of training-oriented and highly equipped cyclists – so-called “lycra-cyclists” [14] – and in such a cycling culture the e-bike is counter to the motivation for cycling (namely that it provides daily exercise). In support of this, a previous study has shown that there is a substantial part of the cycling population that has improved fitness as a main motivation for cycling [16]. However, an equally large segment of the cycling population has been found not to be motivated by fitness. Hence, fitness turns out to be the most divisive issue related to cycling: people tend to think of fitness as either important or unimportant in their decision about mode of travel, and rarely as of middle importance [16].

This disparity in motivation for cycling is a good illustration of the importance of looking not just at extrinsic motivations but also at intrinsic motivations for travel if we are to better predict travel demand [31]. Furthermore, it points to the importance of studying how people differ in their evaluation of different types of motivation. The theory of planned behaviour (TPB) is often used to predict behaviour [2]. Studies have linked socio-psychological variables derived from the TPB such as attitudes, social norms and perceived behavioural control to active transport (cycling and walking) [11, 22, 28]. In a review of psychological determinants of active travel [32] concluded that the TPB variables could predict existing levels of active transport, but often failed to predict changes in behaviour. A promising alternative to the TPB is to look at people’s value systems. Values are “desirable, trans-situational goals, varying in importance, that serve as guiding principles in people’s lives” ([37], p. 269). As opposed to attitudes, that are seen as more superficial and rationally derived, values are believed to be deeper engraved into a person’s sense of self, and hence to function more as sources of intrinsic motivation for behaviour ([37], p. 269). Hedonism is a value within Shwartz’s value system that implies priority to pleasure and sensuous gratification being given as a goal relative to other important goals [35]. People who score highly on Hedonism have been found to be less likely to conduct positive health behaviours [45]. Including information about people’s hedonistic life values within the context of physical activity could improve ability to predict choice of the e-bike as transport mode, and subsequently their potentially increased active mobility (use of non-motorized transport). As far as we know, no previous studies have attempted to link Hedonism to choices about active mobility. A plausible hypothesis in that regard is that people who score high on Hedonism are less likely to engage in physical activity and to travel by active mobility. Further, e-bikes might have a stronger appeal among people who score high on Hedonism.

Adopting a public health perspective, we explore perceptions related to e-bikes and physical activity in the general public, with a particular focus on how these factors relate to hedonistic life values. In this way, we aim to expand our understanding of the potential spread of e-bikes. Furthermore, we test the changes in physical activity of a given individual who obtains an e-bike compared to someone who does not. Finally, we investigate the potential substitution effect, i.e. whether the physical activity that may occur from increased cycling activity is associated with reduced physical activity in other domains.

The SEM model suggests that people who score high on Hedonism are more interested in buying an e-bike and are less physically active than those who score low on Hedonism. People who cycle a lot, both for transport and for exercise, are less interested in buying an e-bike. The results show that e-bike interest is not directly linked to existing physical activity, but that it could be explained via scores on Hedonism. Hence, the results point potentially to a positive health effect, since the e-bike seems to have a stronger appeal among those who have a lower level of physical activity, so-called “couch potatoes”.

The use of the latent construct Hedonism in the SEM model deserves some discussion. Hedonism was included in order to capture what we believed to be a potentially important intrinsic motivation for travel [31] of particular relevance to physical activity and the electric bicycle. The measurement model for this construct was saturated and the psychometric properties of our construct needs to be assessed in relation to the full model. In the full model, all Hedonism items had satisfactory parameter estimate sizes (standardised: 0.3–0.4), and the full model showed acceptable model fit. One previous study has compared the IPSOS/MMI items utilised here with the internationally validated Portrait Value Questionnaire (PVQ) [36], and found that they discriminate similarly across different segments of the population [40], which indicates a certain level of construct validity. However, we cannot be sure that we have really captured well the purported essence (that is of most value) of Hedonism. Future studies should therefore consider using validated questionnaire items such as those from the PVQ in order to verify our results.

It should also be noted that the cycling activity and physical activity variables used in the model were heavily skewed. Excluding respondents who did not cycle or exercise at all would have improved model fit, and was considered but rejected, since these respondents are of particular relevance for the e-bike as an activity generating tool. The bootstrap tests revealed that the inclusion of the skewed variables did not affect the results substantially.

The study then went on to explore the health effect of increased cycling, with the introduction of e-bikes in “real life settings” in light of physical activity and substitution. The results showed that cycling activity (both for transport and for exercise) increased in the two groups of e-bike users while remaining stable (non-significant decrease) in the comparison group. We found that increased cycling for transport (moderate physical activity) leads to more total physical activity in the group of e-bike users compared to a comparison group. The changes for both cycling activity and other physical activity were greatest for those in the FIVH group. Hence, our findings indicate that there is no substantial substitution effect for physical activity with the introduction of e-bikes in a Norwegian cycle population.

For the intervention study, participants were recruited from two different samples – e-bike customers and a selected group of people with marked sedentary behaviour (the FIVH group). For those in the FIVH group, the potential for change in both cycling activity and other physical activity was greater than for the other groups, which might explain the dramatic change in cycling activity. Close follow-up of this group included introduction of the use of the bicycle and three meetings throughout the trial period, may also have contributed to the dramatic increase. In light of this, we needed to address this group as a sub-group of e-bike users, and not representative of the total population of e-bike users. Still, these dramatic results illustrate the potential of the e-bike to induce increased physical activity among those with a sedentary lifestyle who might find a regular bicycle as a non-alternative.

Looking at the group of customers gives a more representative picture. Our results showed that for those who already had moderate cycling activity at baseline (with a regular bike), the increase in cycling activity was significantly higher than for those in the comparison group. The e-bike users replicate their previous cycling activity with a regular bike, but the duration of the activity (numbers of minutes) increases significantly. These results are consistent with previous studies [43] showing that those who purchase an e-bike cycle more often (frequency) and for longer distances (duration) than before.

There were baseline differences between the test groups and the comparison group. The test groups cycled less, had lower employment levels and more females than the comparison group. They also had a somewhat lower bicycle ownership. From previous research we know that e-bikes tend to have a larger appeal (in the form a stated preference) for those who cycle less, and for females [15]. The observed baseline differences (which are the results of an actual purchase decision) functions to confirm these findings.

It should be noted that for all groups the reported levels of PA at T0 were well above recommendations. This might seem to contrast with the results of the SEM analysis, showing that e-bikes seems to appeal to the most sedentary population (one would maybe expect the FIVH group and the customer group to have lower than recommended activity levels). There are two likely explanations for this discrepancy. First, the levels of physical activity could have been systematically overrated, either due to self-report bias, or due to the way in which we calculated the level of PA from the kilometres cycled (into minutes). This explanation is partly supported by the fact that the FIVH group were also recorded as having above recommended values of physical activity, even though this group had been carefully screened in the selection process. Another likely explanation is that the SEM analysis investigates peoples stated preferences for e-bikes, whereas the intervention study looks at the results of revealed preferences (i.e. having bought an e-bike). In other words, those who end up buying an e-bike might have higher activity levels than those who express an interest for one. Even if both explanations are true, our initial conclusion, that the e-bike appeals to “couch potatoes” is still valid, since those who bought an e-bike had lower activity levels than the comparison group at baseline.

The current study looks at the potential public health effects of e-bikes and explores the changes in physical activity of users. While e-bike users increase the amount of cycling they do, as well as the levels of both moderate and vigorous physical activity, no such difference is found in the comparison group. The results show that with increased cycling for transport more total physical activity is accumulated in the e-bike user group compared to a comparison group, hence our findings indicate that there is no substitution effect of physical activity with the introduction of e-bikes in a Norwegian cycle population. The study also looks at perceptions in relation to e-bikes and physical activity among the general public, and shows that the appeal of the e-bike is strongest among those with little existing interest in, or levels of, physical activity. The net effect of the e-bike therefore seems positive from a public health perspective.