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SE Lobben, L Malnes, S Berntsen, LI Tjelta, E Bere, M Kristoffersen, T Mildestvedt
Bicycle usage among inactive adults provided with electrically assisted bicycles
BICYCLE USAGE AMONG INACTIVE ADULTS PROVIDED
WITH ELECTRICALLY ASSISTED BICYCLES
S E. L, L M, S B,
L I T, E B, M K,
T M
1Department of Global Public Health and Primary Care,
University of Bergen, Bergen, Norway
2Department of Public Health, Sport and Nutrition, University of Agder,
Kristiansand, Norway
3Department of Education and Sports Science, University of Stavanger,
Stavanger, Norway
4Department of Sports and Physical Activity, Bergen University College,
Bergen, Norway
ABSTRACT
In the present study we aimed primarily to examine cycling time and
distance when inactive subjects were provided with electrically assisted
bicycles. Secondly to evaluate changes in maximal oxygen uptake. Inactive
employees in a selection of public and private corporations in three Nor-
wegian cities were invited to participate. Inclusion criteria were: a desire
to cycle to work, residence more than 3 km from the workplace, and not
physically active according to guidelines. There were 25 participants in
the study and we provided them all with electrically assisted bicycles fitted
with GPS bike computers to record usage. The participants were followed
for three to eight months, 226 days on average. Measures of maximal
oxygen uptake were performed before and after the intervention. Demo-
graphic characteristics and prior transportation habits were reported in
a questionnaire at baseline. Participants cycled for 107.1± 62min per
week covering 37.6 ± 24 km per week. The distances cycled were signifi-
cantly greater in the autumn (47.4 km/week, p=0.035) than in the spring
(32.1 km/week). Participants cycled more on weekdays (7.1 km/day,
p < 0.001) compared to weekends (0.9 km/day, p<0.001). Maximal oxygen
uptake improved significantly, 2.4 ml/min/kg (7.7 %), p<0.001 and this
was associated with cycling distance (r=0.49, p=0.042) and self-reported
Acta Kinesiologiae Universitatis Tartuensis, 2018. Vol. 24, pp. 60–73
https://doi.org/10.12697/akut.2018.24.05
Bicycle usage among inactive adults provided with electrically assisted bicycles | 61
commuting distance (r=0.51, p=0.018). Offering electrically assisted bicy-
cles to inactive employees may initiate transport-related physical activity
and may give positive health effects.
Keywords: Active commuting, electrically assisted bicycle, maximal oxygen
uptake, physical activity promotion
INTRODUCTION
Physical inactivity is a risk factor comparable to smoking and is a leading
cause of premature death in the 21st century [16]. Inactivity is an important
cause of coronary heart disease, type 2 diabetes, breast cancer and colon
cancer and it caused more than 5.3 million of the 57 million deaths world-
wide in 2008. If physical inactivity decreased by 25 %, more than 1.3 million
deaths could be prevented every year [16]. It is well documented that regular
physical activity (PA) has several benefits [20], and a dose-response relation-
ship between PA and all-cause mortality has been reported [24]. Maximal
oxygen uptake (VO2max), the highest rate at which an individual can consume
oxygen during exercise, limits the capacity to perform aerobic exercise and
is recognized as the best single measure of aerobic fitness [22]. An increase
in VO2max of 3.5 ml/min/kg is associated with a risk reduction of 13% for all-
cause mortality [14]. In addition, VO
2max
has been estimated to be a better
predictor of mortality from cardiovascular disease than other established
risk factors such as pack years of smoking, blood pressure, diabetes and cho-
lesterol [1].
In a Norwegian study, only 20% of adults met the national guidelines for
PA of 150 minutes of moderate or 75 minutes of vigorous intensity PA per
week [11]. In the U.S. only 10% of the adult population met the PA guide-
lines [30]. The effectiveness of interventions to promote and increase PA
have been examined, and there is some evidence that public health efforts
may successfully increase PA [19]. In a Nordic setting, time constraints and
lack of motivation were the two most important reasons to refrain from PA
[25]. Active commuting by bike could be an efficient way to incorporate PA
into modern lives without requiring extra time and planning and it could
have substantial health benefits [6, 8, 21, 23]. Adults who use active modes
of transportation also reported greater total PA [23]. Increased cycling as a
share of overall daily transportation in a population could also have posi-
tive effects through decreased air pollution and greenhouse gas emissions
[4]. Despite the potential positive health effects of cycling, a minority of the
population initiate active commuting by bike. In 2009 cycling contributed
62 | SE Lobben, L Malnes, S Berntsen, LI Tjelta, E Bere, M Kristo ersen, T Mildestvedt
to 4% of short-distance journeys conducted in Norway, a reduction from 5%
in 2005 and 7% in 1992 [17]. Short journeys completed on foot accounted
for 22%, and the rest were by car or public transportation. For the three
cities in the present study, cycling contributed to 9% of short-distance jour-
neys in Kristiansand, 5% in Stavanger, and 3% in Bergen [17]. Prevalence
of commuter cycling varies widely between countries and evidence describ-
ing the types of intervention that will increase commuter cycling remains
sparse [27]. During the past decade, electrically assisted bicycles (EABs)
have become a popular alternative mode of transport [7]. In a Norwegian
setting, 66 participants with a mean age of 47 years, were offered an EAB for
9–64 days and followed with a self-reported travel diary. In this study, the
number of trips and distance cycled increased compared to a group without
access to an EAB. Also, the total share of cycling as a mode of transporta-
tion increased in favour of the EAB group [9]. In addition, recent studies
illustrated that riding an EAB may be classified as moderate-to-vigorous
intensity PA (MVPA) [2, 26]. One previous study found that commuting by
EAB may increase maximal power output and power output at the anaerobic
threshold [3]. However, the intervention lasted for only 6 weeks, and there
was no significant improvement in VO2max among the group of untrained
white-collar workers who participated. Few studies have examined whether
access to an EAB influenced bicycle use and if such activity was associated
with improved fitness.
We aimed primarily to examine cycling time and distance when inactive
subjects were provided with electrically assisted bicycles. Secondly to evalu-
ate changes in maximal oxygen uptake.
MATERIAL AND METHODS
Study design
In the present study, inactive Norwegian adults were given access to an
EAB and a bike computer with GPS (Garmin 500). The study was a pro-
spective quasi-experimental intervention. Before the intervention period,
participants reported demographic characteristics and prior transportation
habits in a questionnaire. Participants recorded cycling frequency and dura-
tion using a GPS bike computer. They were provided with an EAB for up
to eight months, an extra set of studded tyres for winter use, and the ser-
vices of a cycle workshop for repairs. Five types of EAB were distributed
among the 25 participants. Their assisting electric motor was disabled if the
velocity exceeded 25 km/h (15.5 mph) or when the participants stopped
Bicycle usage among inactive adults provided with electrically assisted bicycles | 63
pedalling. This is in accordance with Norwegian legislation and the Euro-
pean Standard. The participants were informed to contact the study leaders
by email or phone if they had technical problems with the bikes or the GPS.
Participants also performed a cardiorespiratory fitness test, measured as
maximal oxygen consumption (VO2max), pre- and post- intervention.
Study population
We recruited 25 participants from three Norwegian cities: Bergen, Stavanger
and Kristiansand, 23 of them in September 2014 and two in March 2015
due to receipt of additional funding. The intervention period therefore was
eight and three months respectively, 226 days on average. Employees in a
selection of public and private corporations were invited to participate in the
study. Corporations invited employees via intranet, local magazines or by
email and then selected participants through convenience sampling. Inclu-
sion criteria were published in the announcement: 1) 18–70 years of age;
2) residence > 3 km from the workplace; 3) a desire to cycle to work most
weekdays; and 4) not engaged in regular PA, such as active commuting or
endurance training for more than 30 min, 2 days per week. The study leader
in each study city, together with a leader from the recruiting company, made
a selection of volunteers who met the inclusion criteria and then held an
information meeting where the inclusion criteria were clarified and written
informed consent was obtained from participants. At this meeting, infor-
mation about safety in traffic, maintenance of the EABs and user guidance
about the GPS device was provided.
In total, 21 participants completed the intervention and performed a
post-test. One of 25 included participants withdrew from the study and 3
others did not perform post-test VO
2max
. Reasons were injury (one), moving
out of town (one), lost to follow-up (one) and withdrawal (one). Three of
the participants did not have valid GPS-measurements (due to technical
failure) and additional three others were excluded from the seasonal com-
parison, one due to GPS-data without a specific date, and two due to the
shorter intervention period of three months. The mean age in the group
without valid GPS-data was 48 years and two were female. The mean age in
the group not eligible for post-testing was 46 years and three were female.
The study was approved by the Regional Committee for Medical
Research Ethics, Health Region West (2014/603). Written informed consent
was obtained from all participants.
64 | SE Lobben, L Malnes, S Berntsen, LI Tjelta, E Bere, M Kristo ersen, T Mildestvedt
Measurements
Participants reported age, gender, education level and commuting distance
from home to work in a questionnaire at baseline. Technical issues regarding
GPS and EABs were reported in a questionnaire at post-test or by reporting
to the study leaders by email.
We recorded bicycle use with GPS (Garmin Edge 500, Southampton, UK)
which measured distance, speed, duration and vertical distance. Recorded
data were uploaded to Garmin Connect’s website (https://connect.garmin.
com/) by the participants and a member of the research team collected the
data at the end of the intervention. Data were manually checked to identify
and eliminate user error. Reasons for excluding GPS data from the analyses
were: loss of GPS signal during the trip or data with no or minimal move-
ment, for instance if parking the bicycle without turning off the computer.
Pre- and post-tests took place in sports laboratories at the associated uni-
versities or colleges in Bergen, Kristiansand, and Stavanger. Direct measures
of VO2max were performed on a treadmill using a stepwise modified Balke
protocol until exhaustion [5]. The test began with a five-minute warm
up with pace at 4.8 km/h and followed an increasing workload by incline
every two minutes until 20% incline, at which point speed increased. We
measured VO2max, minute ventilation (VE) and respiratory exchange ratio
(RER) by open-circuit breath-by-breath indirect calorimetry with mixing
chamber. Heart rate (HR) was registered every minute with a HR sensor
Polar S610i, (Polar Electro, Oy, Kempele, Finland). Time to exhaustion was
measured as the number of minutes from test start (including warm up)
to maximal exhaustion. We used three types of gas analyzers: Oxycon Pro,
(Jaeger GmbH, BeNeLux, Breda, Netherlands) at two of the test centres, and
Vmax 29 (Sensor Medics, Yorba Linda, CA, USA) and Vintus CPX (Care
Fusion, Hochberg, Germany), at pre- and post-test respectively, at one of
the test centres. All gas analysers were calibrated in advance and masks were
checked for leakage when clothed. The test leaders verbally encouraged the
participants to achieve their maximal capacity during the test. Criteria for
acceptable VO2max were the subjective assessment from the test leader that
maximum exertion was achieved.
Body weight was measured to the nearest 0.1 kg. Participants self-
reported height.
Bicycle usage among inactive adults provided with electrically assisted bicycles | 65
Statistical analysis
We used the statistical package SPSS (version 23) for statistical analysis.
Descriptive data are reported as mean and standard deviation (SD), num-
bers and percent in Table I. Results are reported as mean and 95% confi-
dence intervals (CI) in Table II. Cycling distance recorded with the GPS was
transformed using Log10 in order to satisfy normality assumptions. Self-
reported commuting distance from home to work was normally distributed.
Paired samples t-test was used to compare differences between pre-test and
post-test. We analysed the association between variables using Pearson’s cor-
relation coefficient.
RESULTS
The mean age of the 25 participants was 44 years (33–57), and 18 (72%) were
female. Their baseline characteristics are presented in Table 1.
Table 1. Characteristics of study participants at baseline (n=25).
Variable n (%) Mean±SD
Women 18 (72)
Age (years) 44±7
Height (cm) 175±8.6
Weight (kg) 82.2±17.9
Distance to workplace (km) 10.2±3.7
Part time employed 2 (8)
Using car/moped to work 20 (80)
Using public transport to work 5 (20)
Educational level
High School/Elementary School 7 (28)
University/College 18 (72)
Current smoker 3 (12)
Married or live in partner, n (%) 22 (88)
66 | SE Lobben, L Malnes, S Berntsen, LI Tjelta, E Bere, M Kristo ersen, T Mildestvedt
Table2. Maximal oxygen consumption (V
̇O2max) pre- and post-intervention.
N (%) Mean±SD Observed
min-max
CI (95%)
Pretest V
̇O2max (ml/min/kg) 25 (100) 33.1±6.3 20.0–46.9 30.5–35.7
Posttest V
̇O2max (ml/min/kg) 21 (84) 36.5±4.7 27.2–46.2 34.4–38.6
GPS data showed that participants spent on average 107.1±62 minutes
cycling, covering 37.6±24 km per week during the intervention period. They
cycled on average 2 days per week (CI (1.6–2.5)). Distances cycled were
significantly (p=0.035) higher in the autumn (47.4 km/week) than in the
spring (32.1 km/week). There was no significant difference in the distance
cycled in the winter season (36.4 km/week) compared to autumn (p=0.085)
or spring (p=0.175). Participants cycled significantly (p<0.001) more on
weekdays (7.1 km/day) compared to weekends (0.9 km/day). A decline in
cycling activity was observed around holidays when vacation days occurred,
in calendar week 52 (Christmas) and calendar week 14 (Easter).
A significant improvement in V
̇O
2max
of 2.4 ml/min/kg (7.7%), p<0.001
from baseline (34.1 ml/min/kg) to post-test (36.5 ml/min/kg) was associ-
ated with GPS-reported weekly cycling distance (r=0.49, p=0.042) (Figure 1)
and self-reported commuting distance from home to work (r=0.51, p=0.018)
[Figure 2]. One third of the participants improved their V
̇O2max 16±3.3%.
30
20
10
0
–10
0 0.5 1.0 1.5 2.0 2.5
Cycling distance (log km·week–1)
ΔV
̇O2max (%)
n=18 r=0.49, P = 0.042
Figure 1. Correlation between ΔVO2max % and GPS-reported cycling distance.
Bicycle usage among inactive adults provided with electrically assisted bicycles | 67
30
20
10
0
–10
0 5 10 15 20
Self-reported distance (km·week–1)
ΔV
̇O2max (%)
n=21 r=0.51, P = 0.018
Figure 2. Correlation between ΔVO2max % and self-reported commuting distance.
We found a significant negative correlation (r=–0.58, p<0.01) between
pre-test V
̇O2max and absolute improvement in VO2max.
The post-testing dropout group reported a mean commuting distance of
21.0±7.4 km and they had a mean baseline
V
̇O
2max
of 27.7 ml/min/kg, 5.3ml/
min/kg lower than the average.
DISCUSSION
This study describes cycling habits and changes in VO2max in 25 self-
recruited, inactive individuals given access to an EAB. The participants used
their EABs on average for 107.1 minutes and covered a distance of 37.6 km
per week. They had a significant improvement in VO2max, which was associ-
ated with commuting distance reported by questionnaire and with weekly
cycling distance reported by GPS.
Previous studies have described the effects of providing EABs or regular
bicycles on cycling habits and physiological parameters [3, 29]. Studies on
the effect of providing participants with an EAB have observed higher self-
reported cycled distance, over intervention periods of six weeks and three
months, compared to the present study [3, 9].
Differences in cycling distance may be influenced by differences in regis-
tration method between studies. Self-reported data on active travel has been
reported to overestimate compared to GPS data [13]. Objective measure
of travel distance is a strength in our study, even though GPS data may be
prone to signal loss from satellites and poor adherence of participants to
measurement protocols [15]. Four of our participants reported problems
68 | SE Lobben, L Malnes, S Berntsen, LI Tjelta, E Bere, M Kristo ersen, T Mildestvedt
with registration and uploading data from and this may have led to under-
estimation of bicycle usage. Cycling distances in previous studies are all
based on self-reported data [3, 9]. The present intervention period began in
September, while the study by Fyhri and Fearnley began in July [9]. Differ-
ences in aspects such as geographic, seasonal variance and characteristics of
the sample can influence cycled distance with an available EAB. Informa-
tion provided to participants and follow-up strategy are reported differently
between studies. This may also affect differences in reported cycle distance
between studies. In our study the participants were encouraged at the base-
line meeting to use their EAB most weekdays, but they were not followed
individually in a structured manner in order to stimulate increased bicycle
activity.
Adherence to EAB-use was high considering that the intervention
took place in late autumn and winter in Norway with mainly cold and wet
weather. High adherence to EAB use indicates that this may be a feasible
strategy to increase PA among inactive adults. In the present study, we
observed a reduction over time in cycled distance, with a significant differ-
ence between autumn and spring. A lower adherence in cycling in the winter
and spring seasons can only be explained in part by holidays interfering with
the daily routine. An alternative explanation could be that participants did
not maintain their cycling activity due to lack of motivation over time, which
is commonly observed in intervention studies [9]. Both motivation to con-
tinue to measure and upload GPS data and motivation to continue cycling
as time passed may have influenced the downward trend observed over the
eight months of the intervention.
Our study demonstrated improvement in VO2max as opposed to Geus
et al. [3] who investigated the physiological effects of commuting by EAB
and found increased maximal power output and power output in fixed lac-
tate concentrations on an ergometer but observed no changes in VO2max.
However, the study by Geus et al. [3] lasted for only six weeks and short
duration may have underestimated improvement in VO2max [3, 12]. It is not
unexpected that improvements in VO2max in the study by Tjelta et al. [29]
which used regular bicycles were greater than in the present study , as exer-
cise intensity is expected to be higher with a regular bicycle than with an
EAB and the participants were encouraged to cycle as much as possible.
The participants in the study by Tjelta et al. [29] were tested at the end of
summer and cycling dropped during winter. More intensive follow up dur-
ing the study period and different timing for the tests might explain some
of the differences between studies. A negative correlation between pre-test
VO2max and improvement in VO2max was seen in the present study. Higher
Bicycle usage among inactive adults provided with electrically assisted bicycles | 69
intensity PA is more effective in improving VO
2max
and very fit individuals
need greater amounts of activity with vigorous intensity to further improve
VO2max [28]. This means that people with higher levels of pre-test VO2max
may need to choose cycling activity with a more vigorous intensity than the
participants with lower pre-test VO2max in order to achieve improvements in
VO
2max
. We observed an improvement of 2.4 ml/min/kg (7.7 %) in VO
2max
and although this may appear small, findings from a meta-analysis indicate
that even 1 MET, 3.5 ml/min/kg increase in VO2max is associated with a risk
reduction of 13% for mortality and of 15% for cardiovascular diseases [14].
Strengths and limitations
A strength of the present study is the quality of the measurements with
objective assessments of bicycle use with a GPS computer and direct
measurements of VO2max. We included participants from three different
cities with variations in bike infrastructure, topography and weather condi-
tions. Most participants were followed for eight months including several
seasons; autumn, winter and spring. The participants represented a hetero-
genic group concerning age, gender, workplace and travel distance. Feasible
inclusion and recruitment procedures also supported the external validity
of our findings. However, our results must be interpreted with caution with
respect to the study’s limitations. We included a self-recruited group moti-
vated to change to EAB as a preferred mode of transportation to work and
our findings may not be valid for the whole population of physically inac-
tive workers. Even though we did not have protocol-guided contact with
the participants during the study period we still had contact with most par-
ticipants in order to solve any technical problems and to initiate technical
services. Taking part in a study and having interactions with the researchers
may have influenced participants’ motivation to use their EABs [18]. Lack
of GPS data from some participants gives us reason to believe that measured
travel time and distance could be underestimated. Other reported technical
issues such as battery capacity, participant adherence and signal loss should
also be taken into consideration. VO2max-testing in nine participants at one
study centre was conducted with a different device compared to baseline. A
reliability analysis on two test persons tested at four submaximal power out-
puts showed 7 % higher VO2-values using the Vyntus-O2-analyzer as used
during post measures (Bentsen S 2017, unpublished data).
This pilot study had a small sample size and no control group, therefore
we can only examine associations. No causal relationships regarding the
effect of EAB use can be drawn. Even though our findings suggest a clini-
70 | SE Lobben, L Malnes, S Berntsen, LI Tjelta, E Bere, M Kristo ersen, T Mildestvedt
cally relevant increase of VO2max, participants did not report other changes
in PA level that may have contributed to the improvements in VO2max [30].
Familiarisation of the test procedure at baseline might also have contributed
to an increase of VO2max at post-test. Four participants were not eligible for
post-testing. However, pre-test VO2max among these participants was rela-
tively low, but not significantly different from the rest of the group.
Conclusions
The present study suggests that giving access to EABs mobilize inactive
individuals to initiate transport-related PA. We need additional studies to
evaluate the influence on cardiorespiratory fitness.
Implications for practice and further research
To the authors’ knowledge this is the first study to objectively evaluate cycled
distance when participants are given access to an EAB, and the association
between cycled distances and changes in VO
2max
over an extended period.
These findings might be considered as exploratory and may function as a
knowledge base for further research, preferably with a larger samples size
and using a randomised controlled design including EAB, regular bicycles
and a control group. Further studies should also include long term follow-up
in order to describe maintenance of PA after an intervention. Our results can
be used for calculations of statistical power.
To avoid missing data it is important to keep procedures for registration
and reporting of PA easy and accessible. Technology is rapidly progressing
and applications for smartphones opens for cost-efficient registration of PA,
including registration of physiological parameters.
ACKNOWLEDGEMENTS
Funding: The GC Rieber Funds; Bergen Council; Stavanger University
Hospital; Kristiansand Zoo and Amusement park; National Oilwell Varco
Norway AS; General Practitioner-scholarship from the Norwegian Medical
Association.
Bicycle usage among inactive adults provided with electrically assisted bicycles | 71
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Correspondence to:
Thomas Mildestvedt
Department of Global Public Health and Primary Care
University of Bergen, Bergen, Norway.
Phone: +47 55 58 61 63
Email: thomas.mildestvedt@uib.no