5ALos Angeles is known for its smog, but air quality has impro.docx

5A
Los Angeles is known for its smog, but air quality has improved
since the 1990’s and so have children’s lungs. The percent of
children with lung defects has decreased from 8 out of 100 in
1998 to 3.5 out of 100 in 2011 and is applauded as an
environmental success story (N Engl J Med 2015; 372:905-
913). For this forum, you are going to find, read and report on
a scientific, peer-reviewed research article. The topic is air
pollution’s effect on exercise and sports. For example, air
pollution was a big concern for the Olympics held in Beijing,
China and Rio, Brazil. What impact does it have on athletic
performance and health?
You are to find a scientific, peer-reviewed research article. The
best place is the library or PubMed. Do not report on a peer-
reviewed review paper on the topic. Remember, a review paper
summarizes several original research papers. You can use a
review to help you find a scientific study. Also, a research
study paper usually has the following sections: abstract,
introduction, methods, results, conclusions, figures and tables.
A review paper does not have these sections.
In your initial Forum post:
· list your selected article
· provide the reference for your chosen article
· describe the study and the results
· state what the study concludes
· evaluate the article. Do you think the study made appropriate
conclusions from its data? Was the study designed correctly to
address the hypothesis?
· Finally, provide your opinion on the matter. Be sure to justify
your position.
Rundell, Kenneth William. “Effect of Air Pollution on Athlete
Health and Performance.” British Journal of Sports Medicine

46.6 (2012): 407–412. Web.
Gryka, A et al. “Global Warming: Is Weight Loss a
Solution
?” International Journal of Obesity 36.3 (2012): 474–476. Web.
Raherison, C, and Filleul, L. “Asthma in Exercising Children
Exposed to Ozone.” The Lancet 360.9330 (2002): 411–411.
Web.
5b
This week we will have two topics in this forum. You will be
assigned one topic an then you are expected to respond to both
topics in your follow-up posts.
If your last name falls between these letters than your topic is:
· A-M: Topic 1, Qualitative, Quantitative and Mixed Methods
Studies
Topic 1: There are several different types of research data. We
divide these into qualitative, quantitative, and mixed methods.
For this forum, find one example of the study type you are

assigned. Tell the class the following:
· Complete reference of the paper
· The kind of study
· Why you feel it is that type of research
The study type you are assigned is based on the first letter of
your last name. Letter C ( Qualitative)
Study Type
First Letter of Your Last Name
Qualitative
A-D
Quantitative
E-H
Mixed Methods
I-M
Topic 2: There are many different types of study designs. Find
a study design that is one of these: case-controlled studies,
cohort study, randomized controlled trials, meta-analysis, or
case study.
Tell the class the following:
feel it is that type of research
Anguera, M Teresa et al. “The Specificity of Observational
Studies in Physical Activity and Sports Sciences: Moving

Forward in Mixed Methods Research and Proposals for
Achieving Quantitative and Qualitative Symmetry.” Frontiers in
psychology 8 (2017): 2196–2196. Web.
Lee, Sheng Yen. “Analysis of Relationship Marketing Factors
for Sports Centers with Mixed Methods Research.” Asia Pacific
Journal of Marketing and Logistics 30.1 (2018): 182–197. Web.
Review
Br J Sports Med 2012;46:407–412. doi:10.1136/bjsports-2011-
090823 407
ABSTRACT
Unfavourable effects on the respiratory and the cardio-
vascular systems from short-term and long-term
inhalation of air pollution are well documented. Exposure

to freshly generated mixed combustion emissions
such as those observed in proximity to roadways with
high volumes of traffi c and those from ice-resurfacing
equipment are of particular concern. This is because
there is a greater toxicity from freshly generated
whole exhaust than from its component parts. The
particles released from emissions are considered
to cause oxidative damage and infl ammation in the
airways and the vascular system, and may be related to
decreased exercise performance. However, few studies
have examined this aspect. Several papers describe
deleterious effects on health from chronic and acute air

pollution exposure. However, there has been no research
into the effects of long-term exposure to air pollution
on athletic performance and a paucity of studies that
describe the effects of acute exposure on exercise
performance. The current knowledge of exercising in the
high-pollution environment and the consequences that it
may have on athlete performance are reviewed.
Evidence supports unfavourable effects from short-
term and long-term inhalation of air pollution to
the respiratory and the cardiovascular systems.1–10
Combustion-related pollutants such as nitrogen
and sulphur oxides, the ammonium ion, organic
aerosols, particulate matter (PM) and ozone are
of concern. Inhaled PM can be causal to oxidative
stress-related airway and vascular injury. Although
there is ample evidence of short-term and long-term
exposure affecting the respiratory and the cardio-
vascular systems, little data are available demon-

strating the effects of air pollution inhalation on
athlete performance.10–12 During athlete training
and competition, lung deposition of emission-
related pollutants is high because of the increased
ventilation during exercise,13 14 and inhalation of
emission pollutants has been shown to cause the
release of infl ammatory mediators from airway
cells.15 16 Furthermore, the asthmatic response is
worsened by high emission pollution.17 This study
describes combustion-related pollutants that are
of concern to the athlete, examines adverse health
effects of inhaling airborne pollution during exer-
cise and presents current evidence that suggests
that athletic performance is compromised by inha-
lation of emission-related aerosols during exercise.
CATEGORIES OF PARTICULATE MATTER
Exposure to freshly generated mixed combustion
emissions such as those observed in proximity
to high volumes of traffi c is of particular concern
since evidence supports a greater toxicity from
the freshly generated whole exhaust than from its
component parts.15 Further, PM toxicity has been
shown to be related to particle surface area, num-

ber count and particle charge.18
Airborne PM is categorised by aerodynamic
diameter and includes the primary categories of
coarse, fi ne and ultrafi ne particles. Particles larger
than 10 μm are not considered harmful to airways
since they are primarily fi ltered at the nasopha-
ryngeal region. Coarse particles (PM10) include
those between 2.5 and 10 μm in diameter, fi ne
particles (PM2.5) are particles smaller than 2.5 μm
in diameter and ultrafi ne particles include those
less than 0.1 μm diameter.19 The establishment
of a separate category for particles less than 2.5
μm is based on research demonstrating that these
smaller particles are more toxic because of their
deeper penetration within the airways of the lung.
PM less than 1 μm in diameter (PM1) are recorded
in many studies, primarily because of portable
equipment limitations. This size range typically
includes particles in the 0.05 to 1 μm in diameter
and is a suitable representation of freshly gener-
ated particles. Although ultrafi ne PM (PM0.1),
are not yet recognised by the US Environmental
Protection Agency (EPA), they are considered
to be the most harmful.4 13 14 18 20 Ultrafi ne par-

ticle concentrations are high in freshly generated
exhaust and can penetrate deep within the lung,
but rapidly decrease in number count over time by
agglomeration and dispersion (fi gure 1).21
PM from freshly generated exhaust emissions are
likely to be the most toxic because they are high-
est in number count and surface area and are in the
particle size range of 50 to 100 nm (or about 1/1000
the diameter of a human hair) (fi gure 2). Fractional
deposition of these 50 to 100 nm particles occurs
in the alveolar region where exchange with the
circulation may occur. Although coarse and fi ne
particles are monitored by the EPA, most toxicol-
ogy research has investigated ultrafi ne particles
(PM0.1) and fi eld research has primarily measured
PM1. An increased deposition fraction (fraction of
inhaled particles remaining in the lungs after inha-
lation) of PM during exercise has been identifi ed,
with the largest deposition fraction noted for ultra-
fi ne particles. For example, the fractional deposi-
tion13 of PM0.1 is increased 4.5-fold during mild (38
l/min) exercise (fi gure 3).14 For PM2.5, it has been
estimated that 9% is deposited in the lungs with
6% reaching the alveolar region.22 Exercise appears

to increase the deleterious effects of PM inhalation
by deposition, while damaged airway epithelium
Correspondence to
Kenneth William Rundell,
Pharmaxis Inc, Medical
Affairs, One East Uwchlan
Ave, Suite 405, Exton,
Pennsylvania 19341, USA;
[email protected]
Received 1 December 2011
Accepted 11 December 2011
Published Online First
20 January 2012
Effect of air pollution on athlete health and
performance
Kenneth William Rundell
07_bjsports-2011-090823.indd 40707_bjsports-2011-
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090823408
from mechanical stress of high ventilation may enhance par-
ticle infi ltration to the circulatory system.
PM toxicology
The precise toxicological mechanism(s) of inhaled PM has not
been established; however, oxidative stress from exposure is
likely involved. It is thought that inhalation of emission pollut-
ants causes a release of infl ammatory mediators from airway
cells that then enter the circulatory system, causing increased
systemic oxidative stress. A decrease in lung antioxidants from
in vitro carbon black exposure has been identifi ed and suggests
that the epithelial lining fl uid-PM interface may represent an
important initial PM detoxifying step.23 This could be critical
to the exercising athlete as it is known that there is transient
loss
of airway surface liquid from high ventilation of dry air, mak-
ing airway cells more vulnerable to effects of air pollutants. An
acute twofold increase in lung antioxidants of rats exposed to
diesel exhaust PM has been identifi ed,24 suggesting a
protective
role against PM-induced oxidative stress for lung antioxidants.

As a consequence to exercise in high emission pollutants, a
44% decrease in total nitrate and a 40% increase in malondi-
aldehyde in exhaled breath condensate were found, support-
ing formation of the powerful oxidant, peroxynitrite9 from the
reaction of nitric oxide (NO) and superoxide. Alternatively, NO
reacts with glutathione in the lung to form a potent airway
bronchodilator, S-nitrosoglutathione (GSNO).25–27
Studies have shown that reduced GSNO in the asthmatic
airways could support increased leukotriene (LT) production,
while high levels could inhibit LT production.28 Since a pre-
dominantly LT-mediated bronchoconstriction after exercise in
high PM has been shown,29 and marked glutathione depletion
(to ~20% of pre-exposure levels)24 occurs in lung epithelial lin-
ing fl uid after particle exposure, GSNO depletion may be, in
part, responsible for PM-induced LT production.
If present in training and competition environments, freshly
generated particles are of specifi c concern to athletes, and are
likely to be related to the high prevalence of airway disease
among certain athletic populations. The prevalence of exer-
cise-induced bronchoconstriction (EIB), asthma and low rest-
ing lung function is high for athletes who train and compete in
a high PM-emission environments – far exceeding that of the
non-athlete and the low-pollutant-exposed athlete. Pollutants

from auto and truck emissions, high emissions from fossil-fu-
el-powered ice-rink resurfacers and ski-waxing fumes all nega-
tively affect the pulmonary and cardiovascular systems.
Potential consequences of inhaling pollutants during exer-
cise include decreased lung function, increased exacerbations
of asthma/EIB, decreased diffusion capacity, pulmonary
hypertension, cardiovascular effects and decreased perfor-
mance. McCreanor et al17 demonstrated the effect of a 2-h
walk while breathing high-PM/high-ozone air compared with
low-PM/low-ozone air on asthmatic airways. There was a
concurrent signifi cant decrease in forced vital capacity (FVC)
and forced expiratory volume in 1 s (FEV1) from the high-PM/
high-ozone exposure exercise, while lung function remained
unchanged from walking in low-PM/low-ozone air (fi gure 4).
An almost sixfold increase in sputum myeloperoxidase after
the high-PM walk was also observed, suggesting neutrophilic
infl ammation.
The high levels of PM1 observed at athletic fi elds and play-
grounds in close proximity to major highways (fi gure 1) can
affect pulmonary and vascular systems of healthy athletes.
Only 30 min exposure to high-PM (>60 000 particles/cm3)/
high-ozone (106 to 300 ppb) ambient air during exercise

Figure 1 Sixty-two days of particle counts on an athletic fi eld
within
50 m of a high-traffi c road. The x-axis is particle counts of
particles
<1 µm in diameter, those emitted from auto and truck
emissions.
Note that a rather rapid decay in number count is related to the
distance from the source. Redrawn.21
Figure 2 Size distribution in number count of freshly generated
emission particles. Note the largest number count is in the 50–
60-nm
size range.
Figure 3 Total particle deposition after 1-h rest and exercise
while breathing 25 g/m3 ultrafi ne carbon black particulate
matter.
Redrawn.14
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090823 409
20-min of exercise in freshly generated four-cycle exhaust
(PM0.1>300 000 particles • cm
−3, CO<5 ppm).
ATHLETE PM EXPOSURE
Ice-rink air is notoriously high in emission pollutants gener-
ated from combustion-powered ice resurfacers.31 There have
been numerous cases of NO2 and CO poisoning in ice rinks
from ice-resurfacer exhaust emissions. Along with high NO2
and CO, high particle counts from fossil-fuelled ice resurfacers
have been found.31 In one study, particle counts from rinks
resurfaced with electric-powered resurfacers and those resur-
faced with fossil-fuel-powered machines were compared.31
The particle counts in rinks using electric-powered resurfac-
ers were not different to the particle counts of the proximal
ambient air. Those rinks that were resurfaced by fossil-fuelled
machines, however, had particle counts ~30 times greater than
causes a small but signifi cant decrease in lung function in non-
asthmatics (fi gure 5).9

Inhalation of air pollution also has negative effects on the
vascular system. Thirty minutes of exercise in high-PM air
resulted in a basal vasoconstriction of the brachial artery,
disrupted normal vascular endothelial function, decreased
fl ow-mediated dilatation response and a 55% decrease in
re oxygenation of the muscle microcirculation.8 Arteriole dila-
tation was reported to be impaired after pulmonary exposure
to particles and myeloperoxidase was found on adhering neu-
trophils on the vascular endothelial wall.30 It was proposed
that this may affect the infl uence of NO on vascular tone
and that the decreased tissue perfusion of the microvascula-
ture from particle inhalation may compromise muscle func-
tion. Cutrufello et al10 noted an increase in pulmonary artery
pressure as well as disrupted fl ow-mediated dilatation after
Figure 4 Lung function of asthmatic subjects during and after a
2-hour walk in either low or high freshly generated diesel
emissions.
Redrawn.17
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090823410
FEV1, with little change in the FEV1/FVC ratio, is
characteristic
of early asthma development.
For the Nordic and alpine skier, ski-waxing fumes from
daily hot waxing provide a major contribution to exposure.
Many of today’s ski waxes are fl uorinated and have been
shown to have a negative effect on lung function.36 When
applied to skis, a hot iron is used; termed ‘hot waxing’. Hot
waxing results in ultrafi ne fl uorine particles being released
into the air in concentrations 25-fold higher than prewaxing.
Animal studies have shown that these fl uorine particles are
quite toxic.37 Although exposure does not occur during exer-
cise, it does occur on a daily basis beginning early in the skiers
career, when lungs are susceptible to damage. This repeated
exposure in combination with airway damage from high ven-
tilation of cold dry air during competition and training may
contribute to the airway dysfunction observed in Nordic ski-
ers. Of course, the elite skier spends little time in the wax

room, but young developing skiers do, since they wax their
own skis until reaching the elite level. Consequently, there
are many years of exposure to the developing lung on the path
to becoming an elite-level skier.
ATHLETE PERFORMANCE AND PM
Inhalation of high levels of combustion-derived PM during
exercise has been shown to result in reduced exercise perfor-
mance during short-term maximal-intensity cycle ergometry.10
12 A single 6-min exercise bout in high PM failed to reduce
exercise performance; however, a second 6-min of exercise in
high PM 3 days after the fi rst resulted in decreased exercise
performance. This observation supports a delayed infl am-
matory effect from the initial exercise.12 In a separate study,
decreased work accumulation was observed in a high-inten-
sity 6-min cycle ergometer ride that immediately followed
a 20-min high-PM exposure ride at 60% of estimated maxi-
mal heart rate,10 suggesting that a rapid response can occur
within a single 20-min exposure. In this study, low-PM (L)
and high-PM groups (H) were randomised, but L and H pairs
were exercised 1 day apart, to evaluate 24-h effect of exposure.
Unlike the previous study,12 these results showed decreases in
performance in both high-PM rides. This was thought to occur
because the 20-min prework accumulation ride allowed time
for a systemic infl ammatory response to occur, whereas the

earlier study12 did not incorporate that pretime trial 20-min
ride. The performance decreases from exercise in high emis-
sion-generated PM air observed in those studies were about
5%12 and 3%, respectively (fi gure 7).10 The implications of
these studies to the athlete competing in a high-air-pollution
environment suggest that even a 20-min warm-up in high air
pollution will have an impact on subsequent performance.
Further, only one 6-min bout of exercise in high pollution may
have a carryover effect that will decrease the performance in
exercise 3 days later.
Blunted fl ow-mediated dilatation from exercise in freshly
generated four-cycle emission aerosols has been observed
( fi gure 8A).10 12 Signifi cant increases in pulmonary artery
pres-
sure after high-pollution exercise were also noted (fi gure
8B).10
Vascular function was correlated with exercise performance
and accounted for as much as 24.4% of the decline in exercise
performance. The <5% performance decrements are quite sig-
nifi cant to the competing athlete. For example, all but the last
place fi nisher in the 3000-m steeplechase at the 2008 Olympics
were separated by less than 5%. These studies provide evi-
dence that high-PM conditions are likely to affect athletic

Figure 6 Measurements of PM1 at 10 ice rinks demonstrate
signifi cant increases in particulate matter (PM1) after rinks
were
resurfaced by fossil-fuelled machines. Rinks using electric-
powered
machines showed no increase in PM1. Redrawn.
6
Figure 5 Signifi cant change in lung function (forced expiratory
volume in 1 s (FEV1) and FEF25–75) of non-asthmatic subjects
after
30-min of high-particulate matter (PM1) exercise was identifi
ed. No
change in lung function was noted from low-PM1 exposure
exercise
(p=0.0005 for FEV1 and p=0.002 for FEF25–75). Redrawn.
9
proximal ambient air (fi gure 6). As a result of this study, the
Vancouver 2010 Olympic Games used electric-powered ice
resurfacers to ensure acceptable air quality at all 2010 Olympic
ice rinks.

Recent papers examining ice-rink air quality31 and the
relationship to EIB have associated the high prevalence of
airway dysfunction in skating athletes to inhalation of PM1.
6
7 The 20% to 43% prevalence of EIB reported in fi gure skat-
ers, hockey players and short-track-speed skaters32–34 is much
higher than the estimated 10% asthma prevalence in the US
and the reported prevalence for summer Olympic Games, ath-
letes.35 Repeated ventilation of cold/dry air during sport train-
ing and competition, combined with high levels of PM1, may
enhance the expression of or directly cause EIB and airway
damage. Long-term exposure can have signifi cant effects on
resting airway function.6 7 Signifi cant decrease in FVC, FEV1
and FEF25–75 over a 3-year period of daily training in an ice
rink with high PM1 from fossil-fuelled resurfacers in female
hockey players has been identifi ed.6 The decline in FVC and
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090823 411
CONCLUSION
Acute exposure to mixed exhaust aerosols during exercise can
cause decreases in lung and vascular function in healthy and
asthmatic subjects. Chronic exposure to mixed exhaust aerosols
during exercise may result in decreased lung function and may
promote vascular dysfunction, which appear to be related to
increased airway and systemic oxidative stress. The
physiological
effects of high-intensity exercise in high levels of mixed
exhaust
aerosols support the observed compromised performance.
Competing interests None.
Provenance and peer review Commissioned; internally peer
reviewed
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Respiratory effects
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ultrafi ne and fi ne
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(accessed 1 September 2011).
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090823.indd 412 4/4/2012 4:24:05 PM4/4/2012 4:24:05 PM
SHORT COMMUNICATION
Global warming: is weight loss a solution?
A Gryka, J Broom and C Rolland
Centre for Obesity Research and Epidemiology, Faculty of
Health and Social Care, Robert Gordon University, Aberdeen,
UK
The current climate change has been most likely caused by the
increased greenhouse gas emissions. We have looked at the
major greenhouse gas, carbon dioxide (CO2), and estimated the
reduction in the CO2 emissions that would occur with the
theoretical global weight loss. The calculations were based on
our previous weight loss study, investigating the effects of a

low-carbohydrate diet on body weight, body composition and
resting metabolic rate of obese volunteers with type 2 diabetes.
At 6 months, we observed decreases in weight, fat mass, fat free
mass and CO2 production. We estimated that a 10 kg weight
loss of all obese and overweight people would result in a
decrease of 49.560 Mt of CO2 per year, which would equal to
0.2% of
the CO2 emitted globally in 2007. This reduction could help
meet the CO2 emission reduction targets and unquestionably
would be of a great benefit to the global health.
International Journal of Obesity (2012) 36, 474–476;
doi:10.1038/ijo.2011.151; published online 26 July 2011
Keywords: global warming; carbon dioxide; weight loss
Introduction
Climate change resulting from the mean rise in temperature
over the last 100 years has been widely discussed.1 It has been
accepted by the majority of scientists that the change is being
caused by the anthropogenic increase in greenhouse gas

emissions. Greenhouse gases in the atmosphere impair the
earth’s cooling processes, which results in the global rise in
temperature.1 The major greenhouse gas is carbon dioxide
(CO2), which mostly comes from burning of fossil fuels (gas,
oil, coal and other solid fuels). Other sources of CO2 emissions
include iron and steel production, cement manufacture, solid
waste combustion or petrochemical production. In 2007,
burning of fossil fuels and cement manufacture caused
emission of 30 649.36 Mt CO2 globally.
2 Across the world,
fossil fuels are combusted to provide energy to generate
electricity, for transport, business, agriculture and industry. If
the current emissions are not reduced, the global temperature

may rise by 2–7 1C by the end of the century, depending on
the models used.3 This in turn may cause the extinction of
many species, irreversible changes in the ecosystems and
environmental disasters like storms, wildfires, droughts or
floods. Such prognoses bring governments to set targets
for the reduction of CO2 production and support the search
for alternative energy sources.
Humans, apart from indirectly producing CO2 through
the use of fossil fuels and the industry, also produce CO2
during respiration. Consequently, global CO2 emissions
depend on the size of the population. In addition, due to
the fact that CO2 production is proportionate to body mass,
heavier individuals produce more (based on our data, for

every kg of body mass lost, resting metabolic rate (RMR)
dropped by about 18 kcal per day and there was a 1%
reduction in CO2 produced). The post-industrial changes to
human lifestyle and diet have resulted in an obesity
epidemic. Although the knowledge of obesity mechanisms
is quickly expanding and novel obesity treatments are being
developed, the situation on a world population level has
not improved. With the countless unsuccessful efforts to
tackle the obesity problem, it is more and more evident that
the global modification of today’s lifestyles and environ-
ments may be the only possible solution to the obesity
epidemic.

In light of the growing literature on the link between
obesity, type 2 diabetes (T2DM), coronary artery diseases and
climate change,4–8 we thought it would be interesting to
discuss the effect of the global reduction of body mass, in
particular of those individuals who are obese and overweight
on worldwide CO2 emissions. It is clear that an omnipresent
weight loss of all obese and overweight population is as
improbable in the short term as global warming is inevitable
if no action is taken. However, it is essential to model the
effect of population weight loss on CO2 emissions. We have
assumed a 10 kg weight loss, based on our observations as
well as other studies using a low carbohydrate diet for a
6-month period.9

Received 17 December 2010; revised 2 June 2011; accepted 24
June 2011;
published online 26 July 2011
Correspondence: Dr C Rolland, Centre for Obesity Research and
Epidemiology,
Faculty of Health and Social Care, Robert Gordon University,
St Andrew Street,
Aberdeen, AB25 1HG UK.
E-mail: [email protected]
International Journal of Obesity (2012) 36, 474– 476
& 2012 Macmillan Publishers Limited All rights reserved 0307-
0565/12
www.nature.com/ijo
http://dx.doi.org/10.1038/ijo.2011.151
mailto:[email protected]
http://www.nature.com/ijo

Methods
The calculations in the current paper are based on an
observed decrease of resting metabolic rate that occurred
with weight loss in our recent study. The intervention
involved 6 months on a low-carbohydrate, high-protein diet
and included 25 obese volunteers (13 females, 12 males) with
poorly controlled (glycated haemoglobin (HBA)1c47.5%)
T2DM (ISRCTN20400186). CO2 production and body com-
position were assessed at baseline and 6 months. The CO2
production was measured using the Quark RMR (Cosmed,
Rome, Italy). Body composition was measured by air-
displacement plethysmography (Bod Pod, Life Measurement
Inc., Concord, CA, USA). The majority of the variables were
not normally distributed; hence, the Wilcoxon signed-rank

test was used to investigate the 6-month changes in weight,
fat mass (FM), fat-free mass (FFM) and CO2 production.
Analyses were performed with SPSS, version 17.0 (SPSS Inc.,
Chicago, IL, USA).
Results and calculations
The dietary composition of participants on a low-carbohy-
drate/high-protein diet is outlined in Table 1. As expected,
the total energy of the diet was significantly lower during the
study than at baseline. According to our recommendations,
the total amount of carbohydrate, both as grams per day and
as a percent of daily total energy, was lower during the study
than at baseline. Additionally, the amount of protein

increased from 22% to about 30% of total energy levels,
but did not change when expressed in grams per day.
After 6 months of the weight loss programme, we observed
a decrease in weight, FM, FFM and CO2 production (Table 2).
The 6-month change in CO2 production was positively
correlated with the changes in weight (r¼0.506; P¼0.0.12)
and FM (r¼0.517; P¼0.011). The majority of weight lost was
attributed to a decrease in FFM (Table 2), reflecting the
higher protein content of the diet, which was about 30% of
energy intake (Table 1). Weight loss achieved by implement-
ing a normal- or a low-protein diet (that is, 10–15% of
energy), could perhaps induce a higher loss of FFM than a
high-protein diet. Consequently, such a diet would cause an
even bigger drop in RMR and CO2 production, but would not

be beneficial to the health of the individual losing weight.
On the basis of the current data, for every 1 kg of body
mass lost, the CO2 production would decrease 3.2 ml min
�1.
Therefore, an individual who lost 10 kg would produce 32 ml
of CO2 less every minute. This would equal to 168 12 l
(33.04 kg) of CO2 less in a year, compared with what would
be produced without weight loss. In 2008, the global number
of obese and overweight adults over 20 years old was 1.5
billion.10 If all those individuals lost 10 kg and sustained it
for a year, the reduction in CO2 emissions would be 49.56 Mt
CO2 per year. This would equate to 0.2% of CO2 emitted
globally in 2007 by burning of fossil fuels and the

manufacture of cement.2 Analogously, a 5-kg weight loss of
all overweight and obese people would reduce global CO2
emissions by only 0.1%.
Discussion
Our calculations have shown that a 10-kg weight loss of all
overweight and obese people would translate into a 0.2%
Table 1 Changes in diet composition during the low-
carbohydrate/
high-protein weight loss programme (n¼25)
Baseline 6 months Change P-valuea
Energy
Kcal 1845±74 1194±21 �594±600 0.001
Carbohydrate

g per day 164±69 50±25 �108±74.1 o0.001
% Total energy 41±9 22±11 �17.8±12.0 o0.001
Protein
g per day 87±33 79±28 �5.3±32.2 0.882
% Total energy 22±7 30±8 7.9±7.5 o0.001
Fat
g per day 80±44 68±20 �16.4±37.1 0.573
% Total energy 38.5 50.0 10.0±13.9 0.015
Values are expressed as mean±s.d. aSignificance level of the
difference
between baseline and 6 months, Wilcoxon signed-rank test.
Table 2 Changes in weight, fat mass, fat-free mass, resting
metabolic rate
and CO2 production, during the low-carbohydrate/high-protein
weight loss
programme (n¼25)

Baseline 6 months Change P-valuea
Weight (kg)
Males (n¼12) 117.7±19.5 108.0±20.9 �9.7±6.4 0.001
Females (n¼13) 104.6±22.2 94.2±22.1 �10.4±7.8 o0.001
Total 110.9±21.6 100.8±22.2 �10.1±7.0 o0.001
FM (kg)
Males 50.3±13.0 41.1±12.9 �9.2±5.2 o0.001
Females 54.1±17.6 45.7±19.8 �8.8±7.8 0.001
Total 52.4±15.7 43.4±16.5 �9.0±6.5 o0.001
FFM (kg)
Males 67.3±11.8 67.0±12.0 �0.3±1.7 0.266
Females 50.5±7.4 49.3±7.3 �1.6±1.4 o0.001
Total 59.0±12.8 58.1±13.3 �0.9±1.7 0.001
RMR (kcal per day)
Males 2267±451 2033±420 �234±181 0.001
Females 1845±428 1572±345 �274±306 0.002
Total 2048±81 1793±442 �254±250 o0.001

CO2 production (ml min
�1)
Males 258±56 220±45 �37±33 0.001
Females 201±47 173±42 �27±37 0.013
Total 226±58 195±50 �31±34 o0.001
Abbreviations: FFM, fat-free mass; FM, fat mass; RMR, resting
metabolic rate.
Values are expressed as mean±s.d. aSignificance level of the
difference
between baseline and 6 months, Wilcoxon signed ranks test.
Global warming and weight loss
A Gryka et al
475
International Journal of Obesity
reduction in the global CO2 emissions. This percentage

seems small; however, we have looked at personal produc-
tion only. Had we accounted for additional reductions in
CO2 emissions that would likely accompany weight loss, for
example decreases in transport costs, and smaller amounts of
food consumed as suggested by Edwards and Roberts,11 the
total estimated decreases in CO2 production would have
been greater. It could also be argued that the decrease in CO2
production, which accompanies weight loss, would mimic
the benefits of decreasing global population.
The theoretical global weight loss would also be of great
health benefit; halving the risks of developing T2DM and
obesity-related cancers, improving glycemic control in those
with T2DM, and finally improving blood pressure and lipid

profiles.12 Such changes would bring the significant reduc-
tions of healthcare costs and also improvements in general
quality of life.
The targets for CO2 emissions, as specified in the Kyoto
Protocol Reference Manual, vary for different countries and
regions of the world. The UK Low Carbon Transition Plan
suggests lowering the emissions by 18% from the 2008 levels,
or 95.9 Mt CO2 per year, by 2020.
13 A 10-kg weight loss of all
overweight and obese in the UK would account for over 1%
of the CO2 emission reduction target by 2020.
14–16
This estimation was only possible when a number of

assumptions were made. First, we assumed that weight loss
in overweight people would result in the same change in FM
and CO2 production as in the obese. Second, we assumed
that obese and overweight, but otherwise healthy people,
would show the same change in CO2 production with weight
loss, as did obese people with T2DM. Finally, it has been
shown that people with T2DM have higher RMR than those
without,17 and therefore, our calculations may be slightly
overestimated. However, if significant loss of FFM occurred
with weight loss (as may be the case with normal- or low-
protein diets), the decrease in RMR could have been higher,
in which case the current estimations would underestimate
it. Present calculations were not designed to accurately

reflect potential impact of global weight loss on climate
disruption, but to signal an opportunity for addressing
individual, global and environmental benefits of weight loss.
Health and climate change issues seem to be closely
related in the perspective of our future. We agree with
Wilkinson et al.,18 who stated that policies to reduce carbon
emissions and climate change will improve health and well-
being of the people. The opposite should also be true;
tackling lifestyle-related health problems should have a
positive effect on the environment. Universal moderate
weight loss of the overweight and obese would result in an
equivocal influence on the world carbon emissions with

possible effects on climate disruption. Nevertheless, this
relatively small amount could help to meet the CO2 emission
reduction targets and unarguably would be of great benefit
to the human’s health. Moreover, the shift from seeing
weight loss as beneficial for an individual’s health to also
being beneficial for the planet may change attitudes toward
healthy lifestyle. If such benefits were persuasive to govern-
ments across the world, a significant impact on global
warming might be achieved as a consequence.
Conflict of interest
The authors declare no conflict of interest.
Acknowledgements
We thank A Stewart for reading of the manuscript and

critical comments. The study of low-carbohydrate diet was
supported by the Go Lower Company.
References
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change.

Int J Epidemiol 2009; 38: 1137–1140.
12 Turner H, Wass J. Oxford Handbook of Endocrinology and
Diabetes.
Oxford University Press: Oxford, 2002.
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National
Strategy for Climate and Energy. HM Government, The
Stationery
Office: Norwich, 2009.
14 Office for National Statistics. News Release: UK Population
Approaches 62 Million. Crown Copyright: Newport, 2010.
15 The Scottish Government. Scottish Health Survey 2008. The
Scottish Government: Edinburgh, 2009.
16 The NHS Information Centre. Health Survey for England
2008
Volume 1: Physical Activity and Fitness. The NHS Information
Centre: Leeds, 2009.
17 Bitz C, Toubro S, Larsen TM, Harder H, Rennie KL, Jebb SA
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Global warming and weight loss
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widerenvironment/Climatechange/index.htm
http://data.worldbank.org
http://www.who.int/mediacentre/factsheets/fs311/en/index.html
http://www.who.int/mediacentre/factsheets/fs311/en/index.html

Reproduced with permission of the copyright owner. Further
reproduction prohibited without permission.
c.ijo2011151a.pdfGlobal warming: is weight loss a
solutionquestIntroductionMethodsResults and
calculationsDiscussionTable 1 Changes in diet composition
during the low-carbohydrate/high-protein weight loss
programme (n=25)Table 2 Changes in weight, fat mass, fat-free
mass, resting metabolic rate and CO2 production, during the
low-carbohydrate/high-protein weight loss programme
(n=25)Conflict of interestAcknowledgementsReferences
HYPOTHESIS AND THEORY
published: 19 December 2017
doi: 10.3389/fpsyg.2017.02196
Frontiers in Psychology | www.frontiersin.org 1 December 2017
| Volume 8 | Article 2196
Edited by:
Sergio Machado,

Salgado de Oliveira University, Brazil
Reviewed by:
Ludovic Seifert,
Université de Rouen, France
Itay Basevitch,
Anglia Ruskin University,
United Kingdom
*Correspondence:
M. Teresa Anguera
[email protected]
Specialty section:
This article was submitted to
Movement Science and Sport

Psychology,
a section of the journal
Frontiers in Psychology
Received: 01 September 2017
Accepted: 04 December 2017
Published: 19 December 2017
Citation:
Anguera MT, Camerino O,
Castañer M, Sánchez-Algarra P and
Onwuegbuzie AJ (2017) The
Specificity of Observational Studies in
Physical Activity and Sports Sciences:
Moving Forward in Mixed Methods

Research and Proposals for Achieving
Quantitative and Qualitative
Symmetry. Front. Psychol. 8:2196.
doi: 10.3389/fpsyg.2017.02196
The Specificity of Observational
Studies in Physical Activity and
Sports Sciences: Moving Forward in
Mixed Methods Research and
Proposals for Achieving Quantitative
and Qualitative Symmetry
M. Teresa Anguera1*, Oleguer Camerino2, Marta Castañer2,
Pedro Sánchez-Algarra3 and
Anthony J. Onwuegbuzie4, 5
1 Faculty of Psychology, Institute of Neurosciences, University
of Barcelona, Barcelona, Spain, 2 INEFC (National Institute of
Physical Education of Catalonia), IRBLLEIDA (Lleida Institute
for Biomedical Research Dr. Pifarré Foundation), University of

Lleida, Lleida, Spain, 3 Department of Statistics, Faculty of
Biology, University of Barcelona, Barcelona, Spain, 4
Department of
Educational Leadership and Counseling, Sam Houston State
University, Huntsville, TX, United States, 5 Faculty of
Education,
University of Johannesburg, Johannesburg, South Africa
Mixed methods studies are been increasingly applied to a
diversity of fields. In this
paper, we discuss the growing use—and enormous potential—of
mixed methods
research in the field of sport and physical activity. A second
aim is to contribute to
strengthening the characteristics of mixed methods research by
showing how systematic
observation offers rigor within a flexible framework that can be
applied to a wide range

of situations. Observational methodology is characterized by
high scientific rigor and
flexibility throughout its different stages and allows the
objective study of spontaneous
behavior in natural settings, with no external influence. Mixed
methods researchers
need to take bold yet thoughtful decisions regarding both
substantive and procedural
issues. We present three fundamental and complementary ideas
to guide researchers
in this respect: we show why studies of sport and physical
activity that use a mixed
methods research approach should be included in the field of
mixed methods research,
we highlight the numerous possibilities offered by observational
methodology in this field

through the transformation of descriptive data into quantifiable
code matrices, and we
discuss possible solutions for achieving true integration of
qualitative and quantitative
findings.
Keywords: systematic observation, qualitative recording
transformation, qualitative-quantitative integration,
qualitative-quantitative symmetry, sport and physical activity
sciences
Diverse substantive areas have increasingly found their way
into the expanding epistemological
and methodological arsenal applied in mixed methods research
in recent years (Ivankova and
Kawamura, 2010). Mixed methods studies have been defined by
several authors as studies aiming
to integrate qualitative and quantitative elements. Johnson et al.
(2007, p. 123), after analyzing 19
definitions provided by experts in the field, proposed the
following definition: “Mixed methods
research is the type of research in which a researcher or team of

researchers combines elements
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Anguera et al. Observation in Sport and Physical Activity

Sciences
of qualitative and quantitative research approaches (e.g., use
of qualitative and quantitative viewpoints, data collection,
analysis, inference techniques) for the broad purposes of
breadth
and depth of understanding and corroboration.” Empirical
studies undertaken in the field of sport and physical activity
have traditionally largely overlooked the methodological—
and epistemological—opportunities offered by mixed methods
research designs, but a growing number of studies in the
field of sport and physical activity have shown the enormous
potential that these designs offer for studying behaviors related
to individual performance (Camerino et al., 2012c; Iglesias and
Anguera, 2012), team performance (Camerino et al.,
2012b,c,d,e),
use of laterality and motor skills (Castañer et al., 2012),
and use of sports facilities by children (Pérez-López et al.,
2016), to name but a few examples. Settings of this type
contain an enormous conceptual richness to be explored and
methodologically captured, and we believe that the time has
come
to build on lessons learned and continue to move forward.
Although observation and other sources of data have been

given some attention in the mixed methods research literature,
few researchers have applied true observational research
methods. Systematic observation is a scientific procedure for
analyzing perceivable behaviors that occur spontaneously in a
natural setting (Bakeman and Gottman, 1997; Anguera, 2003).
In recent years, however, there has been a surge in the number
of empirical studies involving the application of mixed methods
research designs rooted in systematic observation in the field
of sport and physical activity (Camerino et al., 2012a; Anguera
et al., 2014). For this reason we believe that it is time to
reconsider studies that apply systematic observation through a
mixed method design in sport and physical activity. Examples
include shots in soccer (Maneiro et al., 2017), handball (Freitas
et al., 2010), or basketball (Fernández et al., 2009), corner
kicks and throw-ins (Casal et al., 2015), symmetry of actions
and reactions in fencing (Tarragó et al., 2017), maneuvers in
synchronized swimming (Rodríguez-Zamora et al., 2014), errors
in judo (Gutierrez-Santiago et al., 2013), pace during track
events (Aragón et al., 2017), influence of ball size on children’s
performance in basketball (Lapresa et al., 2013a), use of
gestures
and signals by coaches and physical education teachers
(Castañer
et al., 2013), and compliance with rules and regulations, which
themselves serve as a reference framework. The key to

accurately
capturing these realities lies in the application of an
observational
methodology that consists of the following successive stages:
construction of an ad-hoc observation instrument, computerized
recording and coding of behaviors observed, data quality
control, and quantitative analysis of resulting datasets using
adequate techniques for obtaining structured categorical data (in
particular, lag sequential analysis, polar coordinate analysis,
and
T-pattern detection). Each of these techniques is governed by
methodological rigor and scientific logic (Portell et al., 2015a).
Many studies portrayed as representing mixed methods
research studies are constrained by diverse methodological
shortcomings. However, in our opinion, there are two major
ones: inadequate integration of qualitative and quantitative
data and a lack of symmetry between the two approaches.
Greater symmetry between quantitative and qualitative
approaches is methodologically desirable given the need
to merge both perspectives, although there are obviously
situations in which a greater emphasis on one approach or
another is preferable (Sandelowski et al., 2009). There are
two distinct approaches to asymmetry within the theoretical

framework. The first is a phenomenological approach, or more
specifically, an “enactive or radical-embodiment” approach to
the neuroscience of consciousness (Thompson and Varela, 2001;
Lutz et al., 2002). This approach involves integrating first-
person
(phenomenological) data with neuroimaging data in order
to explore the mutual constraints between these two types of
data described in a different manner. The phenomenological
approach is used in cluster trials where physiological data are
obtained from participants in experimental situations. The
second approach, traditionally viewed as more complex, is the
successful mixing of qualitative and quantitative elements. We
believe that the complexity of this approach lies in the nature
of the data involved and it requires robust solutions to strike a
balance between the qualitative and quantitative elements.
Researchers of systematic observation in the field of sport
and physical exercise fundamentally draw their data from
what could be considered exemplary sources, namely video or
sound recordings of behaviors (i.e., direct observation;
Anguera,
2003) and narratives from in-depth interviews (i.e., indirect
observation; Morales-Sánchez et al., 2014; Anguera et al.,
2017).
Less frequently, they use elicited responses (i.e., responses to

structured or semi-structured interviews –Arias and Anguera,
2017- or questionnaires), simulated data (Manolov and Losada,
2017), and physiological data (Zurutuza et al., 2017). Our aim
in this article, then, is to provide guidance on how to resolve
two of the main shortcomings that undermine mixed methods
research in the field of sport and physical activity—integration
and symmetry of qualitative and quantitative data—and to show
how these solutions could be extrapolated to other fields. In the
following sections, we discuss three fundamental concepts with
the aim of contributing to the ongoing dialog in mixed methods
research and helping this field to advance.
SPORT AND PHYSICAL ACTIVITY AS A
NEW SUBSTANTIVE AREA IN MIXED
METHODS RESEARCH
In the late 1990s, Biddle (1997) found very little diversity in
research methods used in empirical studies in two of the most
prestigious sport and physical activity journals he chose to
study—The Journal of Sport and Exercise Psychology (JSEP),
a leading research journal in the field, and The International
Journal of Sport Psychology (IJSP), which was the first journal
in this field. Most of the quantitative research was based on
regression techniques and discriminant analysis, while most
of the qualitative research drew on interviews and content

analysis. During the same period, Morris (1999) reported that
observational and case studies accounted for just 2% of
scientific
production in this field between 1979 and 1998.
In a study published shortly afterwards, Biddle et al. (2001)
presented a detailed analysis of the methods used in both
Sciences
quantitative and qualitative sport and exercise psychology
research, with a focus on discriminant analysis, hierarchical
regression, stepwise statistical procedures (although it should
be noted that stepwise procedures have been debunked by
numerous statisticians; cf. Thompson, 1995; Onwuegbuzie and
Daniel, 2003), and meta-analysis in the area of quantitative

research and thematic analysis (mostly interviews) in the area of
qualitative research. Biddle et al. (2001) words were
particularly
enlightening:
The extent to which such diverse approaches could or should be
integrated is a matter for the reader to decide. Some have stated
that qualitative and quantitative approaches reflect
fundamentally
different paradigms, such as when people refer to qualitative vs.
quantitative methods. Although there are obvious differences in
the two approaches, there are many cases when the two are
combined. (p. 778)
As we will discuss in the last section, one of the main
shortcomings of studies that involve an attempt to combine the
two approaches is the failure to successfully integrate
qualitative
and quantitative data. This is consistent with Bazeley’s (2010)
conclusion that “there are surprisingly few published studies
reporting results from projects which make more than very
elementary use of the capacity to integrate data and analyses
using computers” (p. 434). True integration in applied studies
is not easy task, but the aim of this paper is to show how
a novel methodological approach grounded within systematic

observation can help to overcome some of the challenges
involved.
Based on our experience and work, we can now confidently
state that the “multifaceted” perspective (Tashakkori and
Teddlie,
2010, p. 274) offered by a mixed methods research approach
(Johnson et al., 2007) is now widely present in the field of
sport and physical activity (van der Roest et al., 2015). An
optimal approach would be to take a wide-angle perspective
while resisting the temptation to pose an overly broad research
question, with the ultimate aim of making future research more
effective.
To gain a better perspective on the use of mixed methods
research in sports and physical activity studies worldwide,
we conducted what Alise and Teddlie (2010) refer to as
prevalence rate studies, which represents “a line of inquiry into
research methods in the social/behavioral sciences [referring
to the proportion of articles using a particular methodological
approach]” (p. 104), which is undertaken by assessing (a) the
prevalence rates of MM [mixed methods] in those fields and
(b) the degree to which disciplines are still dominated by the
traditional postpositivist QUAN [quantitative] approaches” (p.
107). Specifically, we performed a literature search of ISI-

indexed
journals in the Web of Science and the ISI Web of Knowledge
(Journal Citation Reports) to determine the number of articles
applying a mixed methods research approach in this field. We
placed no restrictions on language, year, or geographic location.
Table 1 presents a list of the journals analyzed, together with
their JCR impact factor and the number of articles that used
mixed methods research approaches. They key search term used
was mixed methods and we did not place any limits on
publication
dates, although our results show that the majority of articles
retrieved were published after the year 2000. Our findings show
that, compared with the situation described by Biddle (1997)
and
Morris (1999), a considerable number of ISI-indexed journals
now publish mixed methods research studies. We have included
all studies that, based on their keywords, can be considered
mixed
methods studies from the time the mixed methods movement
emerged. The results from the last 15 years highlight the
growing
number of mixed methods studies published in the field of
sport and physical activity. These studies include a considerable

number of conceptual and methodological papers on different
aspects of mixed methods, which have undoubtedly contributed
to the growth of applied empirical studies in this area. Indeed,
the 203 mixed methods research articles identified among this
set
of 67 journals yielded a mean of 3.03 mixed methods research
articles (SD = 4.98). This represents an important advance, not
only because of the increase in studies of this type, but also
because it shows that prestigious peer-reviewed journals are
now
publishing these studies.
INCLUSION OF PURELY OBSERVATIONAL
SPORTS AND PHYSICAL ACTIVITY
STUDIES IN THE FIELD OF MIXED
METHODS RESEARCH
Studies in the field of sport and physical activity frequently
address immediate research concerns that require a scientific
answer to questions related to multiple aspects of learning,
training, and performance. Such realities are multifaceted in any
field, but we are referring to the specific—and possibly
unique—
case of studies in which the primary and often the only goal is
to capture what is actually happening, with no regard for the

administration of standardized tests or the opinions or feelings
of the agents involved. Studies in the field of sport and physical
activity provide numerous examples of such cases, which, due
to their singularity, we believe deserve special consideration
(Castañer et al., 2013).
Let us imagine, for example, that we are interested in studying
the suitability of a certain tactic in an elite individual or team
competition (e.g., a judo or soccer match). A fitting research
design would be systematically to observe the athlete’s behavior
(systematic direct observation) and to conduct an in-depth
interview with the athlete and/or his or her trainer after the
event (indirect observation). Logically, the responses given by
the athlete or trainer might be different to the information
portrayed by the video recording (referred by Greene et al.,
1989; as initiation, which involves discovering paradoxes and
contradictions that emerge when findings from the two
analytical
strands are compared), because opinions regarding performance
can understandably vary and can be elaborated on in an
interview
situation. To meet the goal of our study, we would need to
merge
the quantitative and qualitative findings by comparing the
results

of the interview (presuming that these are purely qualitative)
with
the information captured in the video recordings (as annotation
of the behaviors observed in the successive images analyzed
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TABLE 1 | Publication of mixed methods research studies in
ISI-Indexed sports
and physical activity journals.
Journal JCR Impact factor Number of
Mixed methods

articles, no.
Adapted Physical Activity Quarterly 1.324 2
American Journal of Sports Medicine 4.362 2
British Journal of Sports Medicine 5.025 4
Clinical Journal of Sport Medicine 2.268 2
Current Sports Medicine Reports 1.552 0
European Journal of Sport Science 1.550 2
European Physical Education Review 0.673 12
European Review of Aging and Physical
Activity
0.676 0
Exercise and Sport Sciences Reviews 4.252 0
Gait and Posture 2.752 0

Human Movement Science 1.598 0
Health Education Research 1.574 16
International Journal of the History of Sport 0.258 0
International Journal of Performance
Analysis in Sport
0.798 1
International Journal of Sport Nutrition and
Exercise Metabolism
2.442 0
International Journal of Sport Finance 0.385 0
International Journal of Sport Psychology 0.485 3
International Journal of Sports Medicine 2.065 0

International Journal of Sports Physiology
and Performance
2.662 1
International Journal of Sports Science
and Coaching
0.480 0
International Review for the Sociology of
Sport
0.953 1
International Review of Sport and Exercise
Psychology
4.526 0
Isokinetics and Exercise Science 0.488 0

Journal of Aging and Physical Activity 1.966 5
Journal of Applied Biomechanics 0.984 0
Journal of Applied Sport Psychology 1.062 3
Journal of Athletic Training 2.017 7
Journal of Biomechanics 2.751 0
Journal of Electromyography and
Kinesiology
1.647 0
Journal of Exercise Science and Fitness 0.333 1
Journal of Human Kinetics 1.029 0
Journal of Motor Behavior 1.418 0
Journal of Physical Activity and Health 2.090 8

Journal of Science and Medicine in Sport 3.194 3
Journal of Sports Science and Medicine 1.025 3
Journal of Sports Sciences 2.246 5
Journal of Teaching in Physical Education 1.021 7
Journal of Science and Medicine in Sport 3.194 3
Journal of Sport and Exercise Psychology 2.185 12
Journal of Sport and Social Issues 0.571 0
Journal of Sport Management 0.718 7
Journal of Sport Rehabilitation 1.276 2
(Continued)
TABLE 1 | Continued
Journal JCR Impact factor Number of
Mixed methods

articles, no.
Journal of Sports Medicine and Physical
Fitness
0.972 0
Journal of Sports Science and Medicine 1.025 3
Journal of Teaching in Physical Education 1.021 7
Journal of Strength and Conditioning
Research
2.075 4
Kinesiology 0.585 1
Medicine and Science in Sports and
Exercise

3.983 5
Medicina dello Sport 0.235 0
Motor Control 1.233 0
Pediatric Exercise Science 1.452 0
Perceptual and Motor Skills 0.546 1
Physical Education and Sport Pedagogy 0.811 6
Physical Therapy in Sport 1.653 0
Proceedings of the Institution of
Mechanical Engineers Part P-Journal of
Sports Engineering and Technology
0.885 0
Psychology of Sport and Exercise 1.896 7
Quality and quantity 0.720 32

Quest 1.017 1
Research in Sports Medicine 1.704 0
Research Quarterly for Exercise and Sport 1.566 9
Revista Internacional de Medicina y
Ciencias de la Actividad Fisica y del
Deporte
0.146 0
Revista de Psicología del Deporte 0.487 5
Scandinavian Journal of Medicine and
Science in Sports
2.896 4
Sociology of Sport Journal 0.750 0

Sport Education and Society 1.288 5
Sports Biomechanics 1.154 0
Sports Medicine 5.038 1
Total Number of Articles − 203
produces a systematized, quantifiable dataset built through the
coding of data guided by a structured ad-hoc observation
instrument).
Although interviews as a research method can sometimes
raise concerns due, for example, to doubts about sample
representativeness (Sandelowski, 1995; Onwuegbuzie, 2003),
this
is not the case in the example described. The issue of interviews
in
observational methodology studies of sport and physical activity
is very different, and poses more serious questions, as
illustrated
by the following example.
Let us now imagine that we are studying the fouls committed
by an athlete in a competition. If we did not modify our

approach,
we would be contrasting a visual record of what actually
happened with the athlete’s interpretation of what happened,
with
the additional risk that this interpretation could be tainted by
considerable cognitive baggage. If the purpose of the study is to
analyze the fouls committed by an athlete, what use is it for the
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athlete to say that he or she did not commit the foul if we have
an image showing the contrary? The discrepancies between the
two realities could be considerable, both in volume and nature,
but that aside, we do not actually need the opinion of the
athlete,
because the answer to our research question lies in the analysis

of fragments of what actually happened. This issue becomes
even
more complicated if we decide to include quantitative data, such
as distances covered, number of steps taken, or heart rate, or if
we administer a personality test before and after the
competition,
because none of this information can shed light on our research
question or enrich our findings.
In our opinion, the ideal solution for situations like this
(which are very common) is to apply the successive steps
defined
within observational methodology. These include selecting
dimensions and subdimensions designed to answer the research
question, taking decisions on segmentation of the observable
date into units, proposing a design for each research objective,
building a purpose-designed observation instrument, creating a
computerized coded dataset that allows the data to be arranged
into matrices of codes, checking the reliability and variability
of
the data collected, and analyzing the behavioral patterns hidden
within the code matrices using robust analytical techniques for
categorical data. Systematic observation is the main procedure
used to collect data in event analysis (Happ et al., 2004) and
there

is ample experience with its use and evidence of its potential
(Anguera, 1979, 2003; Portell et al., 2015b).
The study of spontaneous behavior is characterized by
a richness of information that can only be captured by
video or sound recordings, without elicitation (Anguera and
Hernández-Mendo, 2016), and the possibilities offered in this
area have been greatly enhanced by recent technological
advances. Examples are (a) integration of data through
merging, connecting, and embedding strategies (Plano Clark
and Sanders, 2015); (b) integration of multisensor data
through data fusion (Liggins et al., 2017), which consists
of combining signal- and image-processing techniques with
pattern-recognition techniques and artificial intelligence to
create multimodal databases; (c) integration of heart rate
data captured during exercise with observational data on
physical activity through hidden Markov chains (Castañer et al.,
2017b); and (d) application of deep learning techniques, which
automatically extract multilevel characteristics that maximize
the
identification of predefined behavioral patterns (Ordóñez and
Roggen, 2016). The resulting information is also richer in terms
of veracity, as the data are not tainted by a personal opinion but
based on an objective recording of what happened.

A careful choice of observation units is a central component
of observational research (Anguera and Izquierdo, 2006). The
choice of units in the field of sport will be determined by the
research question and by the rules of the sport, each with its
nuances, and the units must be captured through the careful,
rigorous use of video cameras, which is not without its technical
complexities. In soccer, for example, a move may be a macro-
unit
(with the condition that only the team in possession of the ball
is
observed) but it can also be divided into smaller units
depending
on, for instance, how a given player establishes contact with the
ball or with different team mates or areas of the pitch.
Systematic observation differs from other methods in that the
observation instrument must be built ad-hoc—that is, it must be
purpose-designed in accordance with the theoretical framework
of the study. The main instrument used in studies of this type
combines a field format system and category systems tailored to
the research question (Anguera et al., 2007).
The field format (Sánchez-Algarra and Anguera, 2013)
is a multidimensional system. For each field format, it is
necessary to draw up a catalog of behaviors (a list of mutually

exclusive behaviors for each dimension) that is considered to
be permanently open; it is constructed using a decimal coding
system that allows the behaviors to be hierarchically arranged
according to the degree of molecularization required. The final
dataset acquires the form of a matrix of codes consisting of
columns containing the different dimensions/subdimensions and
rows consisting of the successive units into which the episode
observed has been segmented. The category system (Anguera,
2003) is unidimensional and requires a theoretical framework,
which, combined with empirical information on the situation
being observed, enables the construction of a series of
exhaustive,
mutually exclusive categories. Instruments that combine field
format and category systems aim to harness the strengths of
the two systems (flexibility in the first case and support from
a theoretical framework in the second) and compensate for
their weaknesses (inadequacy of the category system in dynamic
processes and multidimensional studies and weakness of the
field
format system in studies that lack a theoretical framework or in
which this framework has been rejected).
Numerous examples have been described in the literature,
particularly in recent years, and have been applied to a wide
range

of sporting contexts, including motor skill analysis (Castañer
et al., 2009), physical activity (Castañer et al., 2016b), middle-
and long-distance races (Aragón et al., 2015, 2017), basketball
(Fernández et al., 2009), soccer (Jonsson et al., 2006; Castañer
et al., 2016a, 2017a; Casal et al., 2017; Diana et al., 2017), judo
(Gutiérrez-Santiago et al., 2011), hockey (Hernández-Mendo
and Anguera, 2002), futsal (Lapresa et al., 2013b), and kinesics
(Castañer et al., 2013). Ad-hoc instruments have been shown
to be equally effective in amateur (Arana et al., 2013) and elite
(Barreira et al., 2014) sport. The growing use of combined
field-
format/category system instruments has undoubtedly has been
favored by the increase in observational studies in the field
of sport and physical activity. We believe, however, that it is
also attributable to the fact that observational methodology is
widely applicable and offers an optimal balance between rigor
and flexibility.
The number of software programs specifically designed
for observational studies has increased in recent years. Apart
from general-purpose programs, such as Microsoft Excel and
Access, researchers now have access to numerous open-access
programs that can be used to record, to display, and to
analyze data, as well as to perform quality checks. Our
research group has designed several freely accessible software

programs to support the scientific community (Hernández-
Mendo et al., 2014). Examples are LINCE (Gabin et al., 2012;
http://observesport.com), HOISAN (Hernández-Mendo et al.,
2012; http://www.menpas.com), MOTS (Castellano et al., 2008;
http://observesport.com
http://www.menpas.com
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http://www.menpas.com), and SOCCEREYE (Barreira et al.,
2013). Another very useful freeware program that our group has
been systematically using for years to record observational data
and to perform lag sequential analysis is SDIS-GSEG (Bakeman
and Quera, 2011).
The concepts and technicalities of quantification (also

known as quantitizing; Tashakkori and Teddlie, 1998) and
data transformation are a recurrent theme in works written
by eminent figures in the field of mixed methods research
(Sandelowski, 2001; Creswell et al., 2003; Bazeley, 2009b;
Sandelowski et al., 2009). Quantification in observational
methodology is particularly robust, because apart from simple
frequency counts, it contemplates other essential primary
parameters, such as order and duration (Bakeman, 1978;
Anguera
et al., 2001; Bakeman and Quera, 2011), thereby providing the
researcher with the means to map the different components of
a behavior as it occurs. In observational methodology, the term
progressive order of inclusion refers to the fact that frequency
is
the parameter that provides the least information; order
provides
information on frequency and something else (i.e., sequence of
behaviors); and duration provides information on frequency and
order (by adding the number of time units for each occurrence
of
a behavior). This specific consideration of the order parameter
is
crucial for detecting hidden structures through the quantitative
analysis of relationships between different codes in
systematized

observational datasets.
Precisely because it contains information on order and
duration, the initial data set, which is derived from an extremely
rich qualitative component, can be analyzed using a wide
range of quantitative techniques, producing a set of quantitative
results that are then interpreted qualitatively, permitting
seamless integration. With observational methodology, we are
no
longer talking about complementing qualitative and quantitative
findings, but rather about integrating them. As stated by Fetters
(2016), in his article drawing comparisons between
developments
in mixed methods research and the transition from the horseless
carriage to the modern automobile, innovation is both needed
and will occur.
The wide scope of opportunities available for processing
data derived from observation supports the idea that purely
observational studies should be considered as mixed methods
research studies, even though they constitute a somewhat
special
case and do not follow traditional patterns. Although this is a
somewhat controversial topic, as Freshwater (2015, p. 296)
stated,

“disagreement and debate is fundamental to achieving
excellence
in scholarship.”
THE WAY FORWARD: OVERCOMING THE
SHORTCOMINGS OF INTEGRATION AND
SYMMETRY
We are at a critical time for the future of mixed methods
research and we believe that the time has come to stop and
to take stock, just as we do in our everyday lives, as we come
up against different obstacles and challenges. Considering the
relatively recent surge in mixed methods research articles
around
the globe, we believe it is methodologically “healthy” to, as
they say in Spain, “put our finger in the blister” and make a
humble but firm call for reflection on what we believe to be the
two major barriers to the successful implementation of mixed
methods research designs: the lack of integration and the lack
of symmetry.
The Barrier of Integration
Integration of qualitative and quantitative research approaches
is a central theme in the mixed methods research literature,

and the title of a recent editorial by Fetters and Freshwater
(2015b)−1 + 1 = 3—graphically showed that a whole is greater
than is the sum of the individual parts. Although it is
understandable that researchers from a given discipline
typically
will follow the traditions of their research communities, it is
necessary to bear in mind that the respective findings will
be mutually informative—that is, they will talk to each other
(O’Cathain et al., 2010).
Quantitative methods address questions such as causality,
generalizability, and magnitude of effects, whereas qualitative
methodologies are used to develop theories, to describe
occurrences, and to explore the contexts surrounding different
phenomena (Fetters and Freshwater, 2015b). Qualitative
data also can be used to design quantitative instruments
(Onwuegbuzie et al., 2010). Just like in an orchestra, each of
the components in a mixed methods research design has an
important role, but the sum of these components form a greater
whole. However, as Bazeley (2009b) pointed out in an
interesting
study that described how different qualitative and quantitative
methodologies could be positioned along a continuum, not all
types of data or analysis can be integrated.

Although numerous leading figures in the field of mixed
methods research have stressed the importance of integrating
qualitative and quantitative data (Creswell, 2003, 2015;
O’Cathain et al., 2010), a large number of researchers, not
surprisingly, still struggle to merge the two approaches and end
up publishing their results separately.
We believe that the failure to successfully integrate qualitative
and quantitative data is largely due to the nature of the data
involved (Bazeley, 2009b) and that this is where we need to
focus our efforts, through reflection, inquiry, and exploration
of solutions. This lack of data integration might also stem
from quantitative and qualitative research questions that are
addressed separately within a mixed methods research study (cf.
Plano Clark and Badiee, 2010). Qualitative and quantitative
data,
however, can be integrated using what is known as the weaving
approach, which involves presenting the respective findings
together according to a specific theme or concept.
Consequently,
we propose that researchers who encounter difficulties merging
qualitative and quantitative data in studies of sport and physical
activity contemplate an initial exploratory phase in which they
search for ways of weaving together their data, at least until a
suitable methodological solution is found.

The Barrier of Symmetry
Unlike other approaches in experimental studies, which from
an enactive framework (Lutz et al., 2002) show the difference
http://www.menpas.com
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between first-person approaches (based on phenomonological
data) and third-person approaches (based on physiological
and behavioral data obtained objectively using a range of
instruments), enabling thus the problem of asymmetry
to be overcome, in observational studies of spontaneous
behavior in natural settings, where nothing is “artificial” or
“staged,” asymmetry acquires a different meaning, as described
below.

Mixed methods research studies typically involve the adoption
of either a qualitative-dominant or a quantitative-dominant
approach (Onwuegbuzie and Combs, 2010), but an additional
issue is that studies are frequently characterized by a lack of
symmetry between the two approaches. Mixed methods studies
typically focus more on qualitative than quantitative data and
accordingly miss the opportunity to explore the wealth of
information that a quantitative analysis of qualitative data can
provide. Although it is true that some researchers apply robust
statistical methods and even multiple techniques to analyze
quantitative data (Onwuegbuzie et al., 2007), they frequently
fail
to move beyond a descriptive analysis (Ross and Onwuegbuzie,
2014), and consequently miss out on the opportunity to explore
the richness of information within the qualitative component
(Bazeley, 2009a; Onwuegbuzie, 2016).
As a step toward achieving this qualitative-quantitative
symmetry, we agree with Happ et al. (2004) that it is
necessary to quantitize the qualitative data and qualitize the
quantitative data using different event analysis techniques,
such as, for example, segmenting episodes of behavior into
events or measuring duration of behaviors. O’Cathain et al.
(2010) also refer to quantitization and qualitization, but argue

that it is not sufficient simply to use the qualitative data
to inform the quantitative findings, stressing instead the
need to mix together the two types of data to create new
variables.
Onwuegbuzie et al. (2011) identified 58 types of quantitative
analyses, which they grouped into four categories according to
level of complexity: number of independent variables, number
of dependent variables, measurement scales for independent
variables (nominal, ordinal, interval, and ratio scales), and
measurements scale for dependent variables. Obviously, each
of these categories can be further broken down into additional
categories (Ross and Onwuegbuzie, 2014). What we are
proposing in this paper, and with specific reference to
research in the field of sport and physical activity, is
an approach that strengthens the analytical processing of
quantitative data derived from the qualitative component of the
study. The concept of independent and dependent variables,
almost omnipresent in experimental and quasi-experimental
studies, is not relevant to systematic observation, because
this involves observing spontaneous behaviors in natural
settings.
Techniques for analyzing quantitative data obtained from
qualitative research are generally complex. Anguera et al.

(2014) presented a list of the techniques used in sport and
physical activity research. In Table 2, we present an updated
version of this list, which also now includes techniques for
analyzing data from quantitative sources (Sanchez-Algarra,
2006).
Table 2 shows the wide range of possibilities that exist for
analyzing the qualitative and quantitative data that coexist in
observational studies in the field of sport and physical activity.
As the table demonstrates, however, what is novel about our
approach from a mixed methods perspective is the way in which
we integrate or mix the two types of data. Generally speaking,
“there are three ways in which mixing occurs: merging or
converging the two datasets by actually bringing them together,
connecting the two datasets by having one build on the other,
or embedding one data set within the other so that one type
of data provides a supportive role for the other data set”
(Creswell and Plano Clark, 2007, p. 7). For our proposal, we
chose the second form: connecting two databases by having
one build on the other. According to Sandelowski et al. (2009),
this connection can be achieved through transformation, i.e.,
by quantitizing qualitative data or by qualitizing quantitative
data. We use the sequentiality method, shown in the last row
in Table 2, which takes as its starting point the annotation

of the order of occurrence of all the behaviors included in
a given observational dataset. This sequentiality permits the
transformation of initially qualitative data into a format that can
be analyzed quantitatively and robustly, achieving thus
successful
integration.
None of the standard research designs conceptualized for
mixed methods research are applicable to the transformation
of qualitative data (derived from video or sound recordings
in natural settings, or from texts resulting from indirect
observation) into quantitative data for analysis using specific
quantitative techniques, such as variability analysis, comparison
of proportions, categorical variance, log-linear analysis, logit
analysis, lag sequential analysis, polar coordinate analysis, T-
pattern detection, and so forth. Perhaps the use of such
techniques will enable the weaving approach called for in
mixed methods research. Several data analysis techniques
that are specific to the study of sequences of behavior,
such as lag sequential analysis, polar coordinate analysis,
and T-pattern detection, have a particularly important role
in observational methodology due to the assignment of
parameters of frequency, order, and duration to the initial
qualitative data (Anguera et al., 2001; Blanco-Villaseñor et al.,
2003) and thereby providing the necessary conditions for

subsequent quantitative analysis using robust, non-standard,
statistical techniques that offer highly relevant structural
results.
As an epilog, we would like to stress that there is wide
consensus in the mixed methods research field on the value of
using merging, connecting, and embedding strategies to
integrate
qualitative and quantitative data (Plano Clark and Sanders,
2015).
In this article, we have focused on an approach for connecting
these two perspectives and shown that it is perfectly possible
to transform qualitative datasets featuring behaviors whose
order of occurrence has been recorded into matrices of code
that can subsequently be analyzed using powerful quantitative
techniques.

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TABLE 2 | Quantitative analysis techniques for processing
qualitative and quantitative data in studies of sport and physical
activity.
Type of relationship Type of data Quantitative statistical
analysis
Descriptive statistics Measures of central tendency:
Mean
Median
Mode
Measures of dispersion:
Variance
Standard deviation
Coefficient of variation

Normal statistical analysis Quantitative data T-test (one
population)
T-test for comparing means between two groups with
independent data
T-test for comparing means between two groups with paired
data
F-test for equality of variances
Univariate analysis of variance (ANOVA):
One-way ANOVA
Two-way ANOVA (with interaction)
Association Relationship between two categorical variables
Yule’s coefficient (Yule’s Q)
Contingency coefficient C
Chi-square (χ2)

Relationship between several categorical variables Contingency
analysis
Log-linear analysis
Logit analysis
Logit analysis-causal
Logit analysis-Markov
Probit analysis
Logistic regression
Correspondence analysis
Relationship between ordinal variables Contingency tables
Log-linear analysis
Logit analysis
Correspondence analysis

Ballot analysis
Ratios Comparison of proportions
Quantitative data Pearson’s correlation coefficient
Simple linear regression
Multiple linear regression
Partial correlation
Covariance Relationship between a naturally dichotomous
variable and a continuous quantitative variable
Point-biserial correlation coefficient (rbp)
Relationship between an artificially dichotomized
variable and a continuous quantitative variable
Point-biserial correlation coefficient (rb)

Relationship between dichotomized variables Tetrachoric
correlation (rt)
Relationship between dichotomous variables Correlation ϕ
Relationship between ordinal variables Spearman correlation
coefficient (rS)
Kendall rank correlation coefficient
Kendall’s W (coefficient of concordance)
Quantitative data Product-moment Pearson correlation
Simple linear regression model
Multiple linear regression model
Partial correlation
(Continued)

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TABLE 2 | Continued
Type of relationship Type of data Quantitative statistical
analysis
Multivariate analysis of variance
(MANOVA)
Quantitative data One-way MANOVA
Two-way MANOVA (with interaction)
Statistical distances and
dimension reduction

Quantitative data Principal component analysis
Calculation of number of principal components
Geometric interpretation
Qualitative data Principal coordinate analysis (multidimensional
scaling)
Algorithm for calculating principal coordinates
Discriminant analysis Quantitative data Main classification
algorithms: Discriminant analysis. Fisher linear
discriminant analysis and quadratic discriminant analysis:
Minimum or maximum method; Unweighted Pair-Groups
Method
Average (UPGMA) method. Cophenetic correlation.
Canonical correlation analysis Quantitative data Population
canonical correlation analysis
Euclidean distance and Mahalanobis distance

Sequentiality Ordered categorical data analysis Lag sequential
analysis
Polar coordinate analysis
T-pattern detection (temporal patterns)
Quantitative data Time series analysis
Spectral analysis
DISCUSSION
The scientific literature of the past 25 years has presented,
from varying and sometimes opposing standpoints, a wide
spectrum of theoretical positions and empirical findings in
relation to studies that are claimed to represent mixed methods
research studies (López-Fernández and Molina-Azorín, 2011).
Mixed methods research is growing in most disciplines—as
demonstrated by Onwuegbuzie and Corrigan (2016) via their
recent meta-prevalence rate study. And, in our opinion, the field
of sport and physical activity is a particularly fertile area in
which studies merging quantitative and qualitative approaches
have begun to flourish. These studies typically involve the

analysis
of behaviors and motor-related skills in a wide range of sports
and activities that are grounded in a theoretical framework,
can be performed at a professional or amateur level, offer big
learning/training opportunities due to their vast scope, and have
the potential for causing considerable impact in the scientific
community and media at large. They are, as such, particularly
deserving of attention. As shown by our review of the literature,
summarized in Table 1, the volume of mixed methods research
publications in ISI-indexed sports and physical activity journals
varies widely from one journal to the next. Perhaps, as Fetters
(2016) suggests via his analogy of the horseless carriage, we
were
unaware that the many decades of tinkering with mixed methods
would spawn a period of accelerated development. We should
also, however, bear in mind the saying “do not put vintage wine
into new wineskins lest it sour.” That stated, although these
“wineskins” are new, they will have benefited from the
experience
gradually accumulated in multiple substantive areas over the
past
two decades.
As the guiding principle of this paper was to show the

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