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.
Limitations and strengths
A strength of the current study is the fairly large sample size.With certain limitations, it is also representative of the cycling population of Norway. The study does not aim to capture the views and attitudes of the population as a whole, but of people who already own a bicycle and thus are more likely to be interested in purchasing an e-bike.
A further strength is the design, (i.e. prospective repeated measures design with test and comparison groups) A challenge with any study of cycling activity in Norway is the large seasonal variation in cycling. Since the current study is conducted in the same time period as this natural seasonal variation, we need to take this into account when assessing the effect of the e-bike on cycling activity. To do this properly we used a comparison group that was intended to resemble the test group as much as possible, but that was not provided with an e-bike. Hence, we assumed that the largest single change over time was the introduction of an e-bike.
There were baseline differences between the samples. The differences in cycling levels should not be detrimental to the observed effects of an e-bike. The changes in physical activity levels will be lower if people who already cycle much were to obtain an e-bike. There was a small difference in the rate of bicycle ownership. Thus, it could be argued that it was the effect of gaining access to a bicycle of any kind, and not to an e-bike per se, that influenced people’s activity levels. However, even in the group with the lowest ownership (the FIVH group) 18 out 21 participants owned a bicycle. It is unlikely that the dramatic effect that was observed could be isolated to the last three participants.
A potential limitation of the study is the use of self-reported measurements in the form of questionnaire items. Within the field of transport research, travel data are traditionally measured by surveys. A common criticism of these surveys is that people tend to under-report journeys related to walking and cycling, and the reported distances (kilometres and time) are not precise [20]. The distribution of smart phones and the development within app technology imply great potential for collecting both electronic travel data and physical activity data. An aim of future studies should be to include more objective measures for comparison and for validating self-report measures.
The studywas performed in Oslo, Norway where the e-bike market is still quite immature, and should be interpreted with this in mind. First, we studied an urban setting, and our results might not necessarily apply to more rural areas, where limited cycling infrastructure and long travel distances might deter uptake of cycling (even with an e-bike). Second, demographics and travel patterns differ between countries. It is likely that similar results would be obtained in countries with similar characteristics, most notably with similar cycling levels. However, it is not certain whether these results can be replicated in countries where the e-bike already has gained a strong market position.
Another aspect is the short follow up period (4–6 weeks), which is maybe too short to determine sustained behaviour change. Future studies should aim to explore if the changes in PA would remain for a prolonged period.
Although the baseline sample is large, our intervention group counts only 65 persons, which is a limitation. On the other side, the number is not small for an intervention design the physical activity effects are substantial enough to be significant even with this number of participants.
Our measure of physical activity was inspired by the short version of International Physical Activity Questionnaire (IPAQ) [8]. In the validated IPAQ questionnaire the respondents account for how many days they have conducted different types of activities, and approximately how many hours and minutes they “normally” used on one of these days [8]. In our study, we wanted a more detailed report to account for the differences in the activity within 1 week, and the respondents reported how many hours in total they had been physically active during the past 7 days. By doing so, we reduce the challenge of averaging quite infrequent activities for the respondent (what is the “normal duration” of moderate physical activity for a person who has cycled for 20 min on Monday and played football for 90 min on Thursday?)
For walking, a cut-off value 20 min was introduced, to make it easier to remember and hence report. The latter might have contributed to some physical activities not being accounted for and resulting in an underreporting of physical activity. Since the results showed no reduction in physical activity, we argue for the absence of a substitution effect followed by increased cycling activity.
It can be discussed whether the short version of IPAQ is sensitive enough to answer the research questions. The categories are broader than in the long version, and it is possible that those who used e-bikes might have overestimated their activity. From a training-oriented perspective, the latter would be a bigger problem. From a public health perspective (where the threshold is moderate activity), we argue for the measurement of physical activity used in this study to be sensitive enough. Still, a limitation in our study is that we did not address the amount of sedentary behaviour. This would have been an important measurement from a public health perspective, as prolonged time spent in uninterrupted sedentary behaviour can provide health risks independently regardless of engagement in physical exercise [13, 30].
We have explored the health effect of cycling on an e-bike by measuring the amount of time spent on the bicycle. The health effect of physical activity is not merely related to the duration of the activity, but also to the individual’s baseline fitness (i.e. weight, intensity, etc.) [4]. Since these factors were not accounted for in this study, we cannot clearly express the health effect of each individual, but instead estimate the total effect with a public health perspective – which was the aim of our study. Future studies should aim at providing baseline and follow-up measurements of objective fitness to arrive at more precise results, and also potentially to differentiate between different user groups.
Implications
From a theoretical point of view, it could be argued that the construct we have called Hedonism is rather a measure of what is known as self-regulation or self-control in motivational theory. A distinction can be made between maladaptive Hedonism and value-based (Schwarz type) Hedonism [27], where the former can be seen as akin to but still different from the personality trait self-control. In the current study, the items used were borrowed from a survey battery that only loosely relates to Schwarz’s theories about altruistic versus hedonistic values. Still, their substantive meaning was closer to a value-oriented understanding (“for me, it is important to …”) of concept, than to a personality-oriented understanding (“I am a person who …”). The value approach is usually studied in conjunction with environmental behaviour, while the personality approach is typically related to health behaviour. This therefore raises the interesting question whether e-bike purchase is health behaviour or environmental behaviour. Previous research on consumer behaviour for environmental technology has found little or only mixed evidence of environmental motives behind the purchase of, for example, electric cars [44]. Another study, one looking at motives for e-bike purchase, found no effect of environmental values on purchase interest [16]. These results, seen in conjunction, point to e-bike purchase as being motivated more by health concerns than by environmental concerns.
In Norway, there has been discussion about governmental help for e-bike purchase, support in the form of fiscal incentives such as Value-added-tax (VAT) exemptions. Normally, the debate is based on environmental objectives (i.e reduced local and global pollution), but based on these findings we argue that it is possible to support such initiatives just as much with public health in mind, at least in countries were uptake of e-bikes, or cycling levels still are low.