TopSport BLITS

prof dr Bas de Geus
Dept. Human Physiology
“Human beings waged a constant war against
muscular work through their evolutionary history.
By any standards, the battle has largely been won”
(Claude Bouchard)

Physical inactivity is identified as the 4th
leading risk factor for global mortality (6% of
deaths globally)
Physical inactivity
WHO, 2010

Active mobility (walking & cycling) is
recognised, worldwide, as a means to solve
(at least in part) the global physical inactivity
problem


Car is used in 52% of the journeys 1-2 km and
up to 79% of the journeys 5-7,5 km
Bicycle remains very rarely used: best score:
18% for journeys 1-3 km but only 3% of the
journeys 5-7,5 km
BELDAM data;
Cornelis, 2012

Active mobility (cycling), Physical activity and
Health research:
◦ What are the health benefits of cycling?
◦ What are the hazards of cycling (in a city)?
◦ What is the cost-benefit?
www.blits.org
Health
benefits
Air
Pollution
Cycling
Environm
ental &
Psychosoc
Accidents
Intervention studies
Sallis et al., 2006

Ecological models focus on particular behaviors
(e.g., walking for transport as distinct from walking for recreation
exercise and bicycle commuting as distinct from recreational
cycling),
which may be influenced by particular
environmental attributes

Built-environment attributes that have been
found to be associated consistently with active
transportation (walking) choices are proximity to
destinations and the connectivity of street
networks
Sallis et al., 2006
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= measure of ‘how friendly an area is to walking’

Factors influencing walkability:
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presence or absence and quality of footpaths,
sidewalks or other pedestrian rights-of-way,
traffic and road conditions,
land use patterns,
building accessibility,
safety

Examined the relationships between adults'
bicycle use for transport and measures of
neighborhood walkability in 2 settings:
◦ Australian city (Adelaide): low rates of bicycle use
◦ Belgian city (Ghent): high rates of bicycle use

Conclusion: despite large differences in bicycle
ownership and use, living in a high-walkable
neighborhood was associated with significantly
higher odds of bicycle use for transport in both
cities, after adjusting for relevant confounding
factors.
Owen et al., 2010
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Positive environmental factors identified as
being associated with cycling included:
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presence of dedicated cycle routes or paths,
separation of cycling from other traffic,
high population density,
short trip distance,
proximity of a cycle path or green space
Negative environmental factors were:
◦ perceived and objective traffic danger,
◦ long trip distance,
◦ steep inclines and distance from cycle paths
Fraser et al., 2011

Strong positive relationships for:
◦ walkability,
◦ access to shops/services/ work
◦ degree of urbanization (people living in more urbanized areas tended to cycle
more for transportation purposes)
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Possible positive association for:
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Possible negative relationship for:
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No relationships with transportation cycling for:
◦ cycling and walking/cycling facilities
◦ hilliness
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access to public transport,
access to recreation facilities,
traffic- and crime-related safety
aesthetics
Van Holle et al., 2011
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A sample of 343 Flemish adults (43% men)
living at maximum 10 km from their
workplace.
Self-report measures of cycling, demographic
variables, psychosocial variables, selfefficacy, perceived benefits and barriers and
environmental attributes (destination, traffic
variables and facilities at the workplace) of
cycling for transport.
de Geus et al., 2007

Results suggest: when people live in a setting
with adequate bicycle infrastructure (e.g.
Flanders), individual determinants (psychosocial,
self-efficacy, perceived benefits and barriers)
outperform the role of environmental
determinants.
de Geus et al., 2007


Attitudes play a more significant role in mode
choice than has so far been assumed.
Individuals in identical situations and in the
same socio-economic groups choose to
commute using different transport modes 
an individual will base his/her choice not on
an objective situation, but on their perception
of that situation; their eventual decision is
thus also grounded in internal factors.
Heinen et al., 2010
Introduction
Back-ground traffic air pollution – Long-term
Introduction
Micro-environment traffic air pollution – Short-term
Introduction
What do we forget?
Physical effort (VE) of cycling >> car driving
 Inhaled # particles: cyclist >> driver ??
Introduction
Measuring ventilation + exposure:
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Van Wijnen (1995) – Vrijkotte (unpublished):
Bicycle/car ventilation ratio: 2.3
den Breejen (2006) + Rank (2001)
Estimating ventilation (HR) + exposure:
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O’Donoghue (2007): ratio: 2.6
Zuurbier (2009): ratio: 2.1
Exposure to air pollution
Comparison bicycle & car passenger
 measuring Ventilation & Particulate Matter
Materials &
Methods
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N = 55
Measurements:
◦ Max test:
 VO2max, HRmax, Wattmax
◦ Field tests:  Cycling + car passenger
 Breath-by-breath ergospirometry (MetaMax 3B):
 breathing rate, tidal volume
 Minute Ventilation
 Particle Number Conc
(UFP) +
Particulate Matter
(PM10, PM2.5)
Materials &
Methods
• 3 ≠ locations:
• flat – hilly
• polluted – non-polluted
Materials &
Methods
Results
UFP
PM10
(PNC/cm³)
(µg/m³)
100
50,000
90
80
40,000
µg PM10 per m3
#Pt per cm3
70
30,000
20,000
60
50
40
30
20
10,000
10
0
0
Brussels
Values are mean (SD)
LLN
Brussels
Mol
cycling
car
LLN
Mol
Results
Minute Ventilation (VE)
Bike/Car ratio VE
male
female
6
5
l/min
4
3
2
1
0
Brussels
Values are mean (SD)
LLN
Mol
Results
Inhaled quantities: bicycle/car ratio
Brussels
12
LLN
Mol
10
8
6
4
2
0
PNC (#inhaled/meter)
Values are mean (SD)
PM10 (inhaled/km)
PM10 (inhaled/km)
Discussion &
Conclusion
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bike/car PNC, PM ratio =  1
bike/car VE ratio = 4.3
inhaled particles by cyclists 400 - 900% higher
compared to car passengers on the same
trajectory
“Take-away”
message

pollution = time- & space dependent
choose low-traffic trajectory
minimalise physical effort when possible in
polluted environment
weather conditions
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Cycling = physical activity on regular basis
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CO conc – distance to MT
Grange et al., 2014
CO conc – distance to MT
Grange et al., 2014

Increased risk of bicycle accidents:
◦ on-road tram tracks,
◦ bridges without cycling facility,
◦ complex intersections,
◦ proximity to shopping centres or garages,
◦ busy van and truck traffic,
◦ cycle facilities built at intersections and parked vehicles located
next to separated cycle facilities.
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Contraflow cycling is associated with a reduced risk.
Vandenbulcke et al, 2013
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Cycle tracks had the lowest risk, about one ninth
the risk of the reference: major streets with parked
cars and no bike infrastructure;
Risks on major streets were lower without parked
cars and with bike lanes;
Local streets also had lower risks;
Increased risks: streetcar or train tracks, downhill
grades, and construction.
Teschke et al, 2012
Grundy et al,
2009
Exposure and injury incidence density stratified per region
INCIDENCE
Absolute number of accidents
EXPOSURE
Frequency (# of trips)
Time (h)
Distance (km)
INJURY RATE (95% CI)
/1000 trips
/1000 h
/1000 km
Brussels
Flanders
Wallonia
24
32
8
64,982
20,153
325,210
116,262
45,190
909,033
22,920
8,540
160,873
0.354 (0.209-0.499)
1.141 (0.675-1.608)
0.071 (0.042-0.100)
0.267 (0.173-0.361)
0.686 (0.445-0.927)
0.034 (0.022-0.046)
0.349 (0.107-0.591)
0.937 (0.288-1.586)
0.050 (0.015-0.084)

500,000 people make a transition from car to bicycle for short trips
on a daily basis in the Netherlands

By 2040, investments in the range of $138$605 million will result in:
◦ health care cost savings of $388-$594 million,
◦ fuel savings of $143-$218 million,
◦ savings in value of statistical lives of $7-$12 billion

The benefit-cost ratios for health care and
fuel savings are between 3.8 and 1.2 to 1,
and an order of magnitude larger when value
of statistical lives is used.
Gotschi et al, 2011
Cavill et al, 2008
Steinbach et al, 2012
prof dr Bas de Geus
Fac LK, Dept. Human Physiology (MFYS) - VUB
Pleinlaan 2, B-1050 BRUSSELS – BELGIUM
[T] +32 (0)2 629 27 54
[E] [email protected]
[W] www.blits.org