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Bicycle usage among inactive adults provided with electrically assisted bicycles

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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 Norwegian 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. Demographic characteristics and prior transportation habits were reported in a questionnaire at baseline. Participants cycled for 107.1± 62 min per week covering 37.6 ± 24 km per week. The distances cycled were significantly 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 commuting distance (r=0.51, p=0.018). Offering electrically assisted bicycles to inactive employees may initiate transport-related physical activity and may give positive health effects.
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± 62min 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, 5.3ml/
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
... Electronically assisted bicycles (e-bikes) offer self-modulated assistance to reduce torque required to overcome hills, head winds or large distances (McVicar et al., 2022), providing a viable mode of transport for sedentary adults (Adrian, 2008). Previous e-bike interventions have reported high levels of satisfaction associated with performing moderate levels of exercise (Wild et al., 2019), along with improved cardiorespiratory fitness associated with larger distance travelled (Lobben et al., 2019). However, the effect of physical activity conducted on an e-bike on other components of physical health, including musculoskeletal fitness are yet to be fully explored. ...
... A sample size calculation was performed using a linear bivariate regression model expecting a correlation of 0.50 between distance travelled and improvements in cardiorespiratory fitness (Lobben et al., 2019), with an α = 0.05 and a β = 0.80 using G*Power statistical package (Faul et al., 2007). The rationale for using cardiorespiratory fitness is that this was the only outcome reported in an unsupervised trial of similar design (i.e., pre-post cohort study; 12). ...
... Therefore, e-biking at moderate intensity generally would need to be supplemented by other forms of exercise to ensure adherence to the current guidelines for physical activity and to result in improvements in overall health and fitness. This is important because, participants who e-cycled further within this study saw reductions in body mass and DBP and has been associated with improvements in cardiorespiratory fitness (Lobben et al., 2019). Data presented by Zhao et al. (2021), suggested that 130 min/week of moderate intensity unassisted cycling would provide benefits for risk of all-cause and cardiovascular disease mortality. ...
Article
Introduction Electrically assisted bikes (E-bikes) have the potential to assist with the accumulation of moderate intensity physical activity but the relationship between volume of e-cycling and health has not been fully examined. The aim of this study was to explore the associations between distance travelled during a 4-week e-bike intervention and measures of health. A second aim was to explore individual responses to using e-bikes and the potential effects of weather on the volume of e-cycling. Methods Twenty-six sedentary adults were assessed before and after 4-weeks using a motion activated e-bike. Health (i.e., blood glucose and blood pressure), cardiorespiratory fitness and musculoskeletal fitness (i.e., lower body strength, power and flexibility) outcomes were obtained before and after the 4-weeks. Travel distance, total monthly rainfall, max and min temperatures (monthly averages) data were collected. Correlations between travel distance and health (body mass, blood glucose, systolic blood pressure, diastolic blood pressure), cardiorespiratory fitness (power output and heart rate (HR) during the Astrand Rhyming test, and predicted VO2max) and musculoskeletal fitness (sit and reach distance, vertical jump height, wall squat time) were analysed. Magnitude of changes relative to baseline values were explored to identify individuals that could potentially benefit more from the intervention. Results An inverse relationship between travel distance was observed with changes in body mass (p = 0.02 and ρ = −0.46) and diastolic blood pressure (p = 0.02 and ρ = −0.44). Individuals with higher blood glucose and poorer vertical jump performance at baseline had better magnitude of change results after the 4-weeks e-cycling. Conclusions Associations between more travel with an e-bike and greater reductions in body mass and diastolic blood pressure were observed. E-cycling has the potential to assist those with poorer health outcomes, but it may need to be supplemented by additional forms of exercise to ensure adherence to the guidelines for physical activity.
... Unlike bike sharing, which mainly aims to resolve the last-mile connectivity problem and is used for recreation and exercise (Hiselius and Svensson, 2017;Ling et al., 2017), e-bike sharing plays a more critical role as a utilitarian transport mode (Ling et al., 2017;Lobben et al., 2018;Sundfør & Fyhri, 2017), resulting in the potential to become an alternative to short-and medium-distance car trips (Haustein & Møller, 2016;Ioakimidis et al., 2016;Moser et al., 2018). Numerous survey-based studies suggested that e-bikes can be used for various purposes, including commuting, shopping, running errands, and recreation (He et al., 2019;Langford, 2013;Munkácsy & Monzón, 2017). ...
... One study on overweight or obese individuals living in regional Australia found that e-bikes provided a moderate level of physical activity and self-perceived improvements in physical and mental wellbeing Anderson et al. 2022 . These findings align with those of the Lobben et al. 2018 Norwegian study, which found that previously inactive participants who began using e-bikes experienced lower blood pressure and heart ratessimilar to the benefits of conventional cycling. The Bourne et al. 2018 review of 17 studies provided moderate evidence that e-bikes can improve cardiorespiratory fitness in physically inactive individuals, and thus offer an alternative to conventional cycling. ...
Technical Report
Full-text available
Healthy, Regenerative and Just includes a call for accelerated transition to healthy, equitable and low-emission transport. This means moving away from heavy reliance on fossil fuel based car and truck transport towards a healthier mix of transport options as well as the electrification of the nation’s road transport fleet. Together these actions will reduce emissions while improving overall health and wellbeing. This rapid evidence review seeks to delve deeper into the challenges and opportunities for health and climate through decarbonisation of the transport sector. It analyses the interaction between transport, health and climate and sets out a series of evidence based recommendations for how Australia can move forward for the wellbeing of current and future generations. The evidence here provides a clear foundation for the health, transport and environment sectors to take action.
... In Germany, sales of e-bikes quadrupled between 2015 and 2020, from 500,000 to nearly 2 million per year (Zweirad-Industrie-Verband, 2016;Brust, 2021), making it one of the most important markets globally after China. However, while several studies point to the positive impact of e-bikes on individual health (Castro et al., 2019;Lobben et al., 2019), their benefit for the mobility system in Germany remains unclear. ...
... Several studies showed significant improvements in a variety of health and fitness related outcomes. Studies showed significant improvements in maximal exercise capacity after 4 weeks to 20 months of PAEB use (Peterman, 2017;Peterman et al., 2016;Lobben et al., 2018;Cooper et al., 2018;Hochsmann et al., 2018). Two studies showed improvement in oral glucose tolerance test responses (Peterman, 2017;Peterman et al., 2016), one was associated with better mental health , and two reported greater enjoyment (Langford et al., 2017;Sperlich et al., 2012). ...
Article
Bicycles with integrated electric motors that require user effort, that is, pedal-assist e-bikes (PAEB), are increasing in popularity. There are several significant health benefits and benefits to our environment that can be attained by increasing use of PAEB. The purpose of this review was to synthesize the literature available on PAEB and to identify future directions for research, and policy and infrastructure development, that ensures an inclusive approach. We conducted a scoping review of the literature that led to the identification of 107 articles that included PAEB. Studies were grouped according to themes: Energy and Emissions, Bike Sharing, Violations and Accidents, Physical Activity, Active Commuting, and Perceptions. Overall, it appears that the uptake of PAEB leads to a modal shift such that overall car use is decreased. PAEB use is associated with lower emissions compared to cars, but requires physical effort that classifies use of a PAEB as moderate intensity physical activity. Cost appears to be prohibitive, thus sharing or rental programs, and subsidies may be beneficial. Several additional barriers related to lack of infrastructure were also noted. Importantly, violations, injuries, and crashes appear to be similar between PAEB users and traditional bicycle users. PAEB offer an opportunity to improve health and mobility in an eco-friendly manner compared to cars. Infrastructure and policies are needed to support this modal shift. There is an immediate need to clearly define PAEBs, and to ensure regulations are similar between PAEB and traditional bicycles. Future research is needed to better understand long-term behaviour change with regards to commuting, and to identify the effect of implementing better infrastructure and policies on PAEB uptake.
... Intervention studies have looked at e-cycling in a population over time (De Geus et al., 2013;Hö chsmann et al., 2018;Lobben et al., 2018;Peterman et al., 2016) Peterman et al. (2016 conducted a study with 20 sedentary commuters, and found that after four weeks of e-bike commuting participants improved their glucose tolerance, VO 2 max (8% increment), and maximal power output. These results indicate that for inactive individuals, cardiorespiratory fitness could be improved. ...
Article
E-bikes or pedelecs are bikes with functional pedals, assisted with an electric motor. Given that the e-bike can help people overcome known barriers to cycling, they might be positive for public health. E-bike users get less exercise than conventional cyclists, everything else equal. Still, people with e-bikes increase their total amount of cycling. Even if some of this cycling replaces former walking or conventional bike trips, the total time spent on active mobility is increased. Other physical activity is not significantly affected when starting to use an e-bike. The e-bike caters for novice cyclists. One challenge is that their lack of previous cycling experience might be a risk factor. There is no proven risk difference between e-bikes and conventional bikes. The health benefit of increased physical activity accumulated through cycling (with any bike) is in any case considered as higher than the negative outcome from injuries.
Article
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The objective of the present study is to review and meta-analyze the effect of E-cycling on health outcomes. We included longitudinal experimental and cohort studies investigating the effect of E-cycling on health outcomes. The studies were identified from the seven electronic databases: Web of Science, Scopus, Medline, Embase, PsycINFO, Cinahl and SportDiscus and risk of bias was assessed with the revised Cochrane Collaboration Risk of Bias Tool (RoB2). We performed meta-analysis with random effects models on outcomes presented in more than one study. Our study includes one randomized controlled trial, five quasi experimental trials and two longitudinal cohort studies. The trials included 214 subjects of whom 77 were included in control groups, and the cohort studies included 10,222 respondents at baseline. Maximal oxygen consumption and maximal power output were assessed in four and tree trials including 78 and 57 subjects, respectively. E-cycling increased maximal oxygen consumption and maximal power output with 0.48 SMD (95%CI 0.16–0.80) and 0.62 SMD (95%CI 0.24–0.99). One trial reported a decrease in 2-h post plasma glucoses from 5.53 ± 1.18 to 5.03 ± 0.91 mmol L−1 and one cohort study reported that obese respondents performed 0.21 times more trips on E-bike than respondents with normal weight. All the included studies had a high risk of bias due to flaws in randomization. However, the outcomes investigated in most studies showed that E-cycling can improve health.
Chapter
Since the mid-2000s there has been a substantial increase in electric bicycle related research seeking to answer a variety of questions in domains ranging from engineering to health. This chapter explores several of these questions, bringing together information on electric bicycle usage and sales, literature examining the demographics of electric bicycle users and the impact of electric bike use on transport, the environment, health, and safety. The chapter also highlights how e-bikes are being promoted across the world and identifies future research priorities.
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Introduction: Pedal assisted electric-bikes (e-bikes), are bicycles fitted with electric motors. Motorised functions on e-bikes only operate when the user pedals, allowing riders a moderate amount of physical activity. This study aimed to explore the mental and physical health and wellbeing impacts related to e-bike usage for inactive overweight or obese individuals living in regional Australia. Methods: Twenty inactive, overweight/obese people who seldom cycled were provided with an e-bike over a 12-week period. Individual semi-structured interviews conducted at the end of the trial generated data about participants' experiences of using e-bikes. Inductive thematic analysis of interview data using Thomas (2006) data analysis framework and NVivo 12 software was undertaken. Results: Data analysis revealed that e-cycling improved participants' mental and physical wellbeing and that they felt happier when riding an e-bike. Conclusions: Riding an e-bike can improve mental and physical health, happiness and overall sense of wellbeing. Greater uptake of e-bikes would have positive health implications for the wider community. Results from this study can be used to inform active transport policy. SO WHAT?: Our study demonstrated that encouraging active transport in the form of e-cycling can improve the overall health and wellbeing of overweight and obese Australians. More specifically, e-cycling demonstrated a positive impact on mental health wellbeing.
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Background: Active travel is associated with greater physical activity, but there is a dearth of research examining this relationship over time. We examined the longitudinal associations between change in time spent in active commuting and changes in recreational and total physical activity. Methods: Adult commuters working in Cambridge, United Kingdom completed questionnaires in 2009 and 2012, and a sub-set completed objective physical activity monitoring in 2010 and 2012. Commuting was assessed using a validated seven-day travel to work record. Moderate-to-vigorous physical activity was assessed using the Recent Physical Activity Questionnaire and combined heart rate and movement sensing. We used multivariable multinomial logistic regression models to examine associations between change in time spent in active commuting and tertiles of changes in time spent in recreational and total physical activity. Results: Four hundred sixty-nine participants (67 % female, mean age 44 years) provided valid travel and self-reported physical activity data. Seventy-one participants (54 % female, mean age 45 years) provided valid travel and objectively measured physical activity data. A decrease in active commuting was associated with a greater likelihood of a decrease in self-reported total physical activity (relative risk ratio [RRR] 2.1, 95 % CI 1.1, 4.1). Correspondingly, an increase in active commuting was associated with a borderline significantly greater likelihood of an increase in self-reported total physical activity (RRR 1.8, 95 % CI 1.0, 3.4). No associations were seen between change in time spent in active commuting and change in time spent in either self-reported recreational physical activity or objectively measured physical activity. Conclusions: Changes in active commuting were associated with commensurate changes in total self-reported physical activity and we found no compensatory changes in self-reported recreational physical activity. Promoting active commuting has potential as a public health strategy to increase physical activity. Future longitudinal research would be useful to verify these findings.
Article
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Objective To identify interventions that will increase commuter cycling. Setting All settings where commuter cycling might take place. Participants Adults (aged 18+) in any country. Interventions Individual, group or environmental interventions including policies and infrastructure. Primary and secondary outcome measures A wide range of ‘changes in commuter cycling’ indicators, including frequency of cycling, change in workforce commuting mode, change in commuting population transport mode, use of infrastructure by defined populations and population modal shift. Results 12 studies from 6 countries (6 from the UK, 2 from Australia, 1 each from Sweden, Ireland, New Zealand and the USA) met the inclusion criteria. Of those, 2 studies were randomised control trials and the remainder preintervention and postintervention studies. The majority of studies (n=7) evaluated individual-based or group-based interventions and the rest environmental interventions. Individual-based or group-based interventions in 6/7 studies were found to increase commuter cycling of which the effect was significant in only 3/6 studies. Environmental interventions, however, had small but positive effects in much larger but more difficult to define populations. Almost all studies had substantial loss to follow-up. Conclusions Despite commuter cycling prevalence varying widely between countries, robust evidence of what interventions will increase commuter cycling in low cycling prevalence nations is sparse. Wider environmental interventions that make cycling conducive appear to reach out to hard to define but larger populations. This could mean that environmental interventions, despite their small positive effects, have greater public health significance than individual-based or group-based measures because those interventions encourage a larger number of people to integrate physical activity into their everyday lives.
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Electric bicycles (e-bikes) represent one of the fastest growing segments of the transport market. Over 31 million e-bikes were sold in 2012. Research has followed this growth and this paper provides a synthesis of the most pertinent themes emerging over the past on the burgeoning topic of e-bikes. The focus is transport rather than recreational e-bike research, as well as the most critical research gaps requiring attention. China leads the world in e-bike sales, followed by the Netherlands and Germany. E-bikes can maintain speed with less effort. E-bikes are found to increase bicycle usage. E-bikes have the potential to displace conventional motorised (internal combustion) modes, but there are open questions about their role in displacing traditional bicycles. E-bikes have been shown to provide health benefits and an order of magnitude less carbon dioxide than a car travelling the same distance. Safety issues have emerged as a policy issue in several jurisdictions and e-bike numbers are now approaching levels in which adequate safety data are able to be collected. Research on e-bikes is still in its infancy. As e-bike usage continues to grow, so too will the need for further research, in order to provide the necessary data to inform policy-makers and industry.
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The aim of this study was to investigate how previously inactive adults who had participated in a structured, partly supervised 6-week exercise program restructured their time budgets when the program ended. Using a randomised controlled trial design, 129 previously inactive adults were recruited and randomly allocated to one of three groups: a Moderate or Extensive six-week physical activity intervention (150 and 300 additional minutes of exercise per week, respectively) or a Control group. Additional physical activity was accumulated through both group and individual exercise sessions with a wide range of activities. Use of time and time spent in energy expenditure zones was measured using a computerised 24-h self-report recall instrument, the Multimedia Activity Recall for Children and Adults, and accelerometry at baseline, mid- and end-program and at 3- and 6-months follow up. At final follow up, all significant changes in time use domains had returned to within 20 minutes of baseline levels (Physical Activity 1-2 min/d, Active Transport 3-9 min/d, Self-Care 0-2 min/d, Television/Videogames 13-18 min/d in the Moderate and Extensive group, relative to Controls, respectively, p>0.05). Similarly, all significant changes in time spent in the moderate energy expenditure zone had returned to within 1-3 min/d baseline levels (p>0.05), however time spent in vigorous physical activity according to accelerometry estimates remained elevated, although the changes were small in magnitude (1 min/d in the Moderate and Extensive groups, relative to Controls, p=0.01). The results of this study demonstrate strong recidivist patterns in physical activity, but also in other aspects of time use. In designing and determining the effectiveness of exercise interventions, future studies would benefit from considering the whole profile of time use, rather than focusing on individual activities. Trial Registration Australian New Zealand Clinical Trials Registry ACTRN12610000248066
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To describe different end criteria for reaching maximal oxygen uptake (VO2max) during a continuous graded exercise test on the treadmill, and to explore the manner by which different end criteria have an impact on the magnitude of the VO2max result. A sample of 861 individuals (390 women) aged 20-85 years performed an exercise test on a treadmill until exhaustion. Gas exchange, heart rate, blood lactate concentration and Borg Scale6-20 rating were measured, and the impact of different end criteria on VO2max was studied;VO2 leveling off, maximal heart rate (HRmax), different levels of respiratory exchange ratio (RER), and postexercise blood lactate concentration. Eight hundred and four healthy participants (93%) fulfilled the exercise test until voluntary exhaustion. There were no sex-related differences in HRmax, RER, or Borg Scale rating, whereas blood lactate concentration was 18% lower in women (P<0.001). Forty-two percent of the participants achieved a plateau in VO2; these individuals had 5% higher ventilation (P = 0.033), 4% higher RER (P<0.001), and 5% higher blood lactate concentration (P = 0.047) compared with participants who did not reach a VO2 plateau. When using RER ≥1.15 or blood lactate concentration ≥8.0 mmol•L(-1), VO2max was 4% (P = 0.012) and 10% greater (P<0.001), respectively. A blood lactate concentration ≥8.0 mmol•L(-1) excluded 63% of the participants in the 50-85-year-old cohort. A range of typical end criteria are presented in a random sample of subjects aged 20-85 years. The choice of end criteria will have an impact on the number of the participants as well as the VO2max outcome. Suggestions for new recommendations are given.
Technical Report
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In the Norwegian Travel Survey 2009, about 29 000 people from 13 years have been interviewed. The survey provides information on travel frequency, trip purposes and travel mode, and on how travel behaviour varies with age, gender, income, place of residence etc. In 2009 the average citizen made 3.3 trips per day. Most trips are short, 42 per cent being shorter than three kilometres. The car is used on 63 per cent of the daily trips, either as driver, 52 per cent, or as a passenger, 11 per cent. 4 per cent are made by bicycle and 22 per cent on foot, while 10 per cent are carried out by public transport. During a month, 53 per cent of the population makes one or more long distance journeys (trips of 100 km or longer one-way and trips abroad). Within Norway 68 per cent of these trips are made by car, while 15 per cent are made by plane.
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This study aims to (1) elucidate whether the Hawthorne effect exists, (2) explore under what conditions, and (3) estimate the size of any such effect. This systematic review summarizes and evaluates the strength of available evidence on the Hawthorne effect. An inclusive definition of any form of research artifact on behavior using this label, and without cointerventions, was adopted. Nineteen purposively designed studies were included, providing quantitative data on the size of the effect in eight randomized controlled trials, five quasiexperimental studies, and six observational evaluations of reporting on one's behavior by answering questions or being directly observed and being aware of being studied. Although all but one study was undertaken within health sciences, study methods, contexts, and findings were highly heterogeneous. Most studies reported some evidence of an effect, although significant biases are judged likely because of the complexity of the evaluation object. Consequences of research participation for behaviors being investigated do exist, although little can be securely known about the conditions under which they operate, their mechanisms of effects, or their magnitudes. New concepts are needed to guide empirical studies.