Discussion Assignment Sports ScienceA+ work, no plagarism

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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; ken.rundell@pharmaxis.com

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

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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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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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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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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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11 These fi nd- ings may suggest an even greater vulnerability among females and should be considered when making exercise recommenda- tions to this population.

The response to PM0.1 inhalation 24 h after exposure in healthy humans demonstrated signifi cant increase in diastolic blood pressure, reduced heart rate variability and signifi cant impaired vasodilatation of the brachial artery.8 Likewise, near infrared spectroscopy demonstrated a decrease in the reoxy- genation slope after cuff ischaemia, suggesting a constrictive response in the microcirculation that supports a physiologically signifi cant decrease in blood fl ow that could affect exercise performance.8

The vascular dysfunction associated with PM inhalation suggests reasonable means by which PM inhalation may result in a cardiovascular incident due to the increased load on the heart. However, this added stress on the heart is not likely to result in a cardiovascular incident in the healthy pop- ulation, but may affect cardiac output and thus performance. Interestingly, just as LT receptor antagonists have been useful in the treatment of asthma, montelukast has been found to attenuate the vascular dysfunction associated with the bra- chial artery after PM inhalation.38 This protection against the vascular dysfunction associated with PM inhalation could potentially improve exercise performance in high-PM conditions.

Figure 7 Short-term exercise performance as work accumulated in a 6-min all-out cycle ergometer ride in low-particulate matter (PM1) and high PM1. Note the signifi cant difference in performance in high PM1. Data were taken from two separate studies.10 12 Figure 8 (A, B) Pre-exercise and postexercise fl ow-mediated

dilatation (FMD) of the brachial artery for four trials of 30-min cycle ergometry in low-particulate matter (PM) and high-PM-emission air. FMD was signifi cantly less for high-PM versus low-PM trials (p<0.05). Pre-exercise and postexercise pulmonary artery pressure was signifi cantly greater in high PM versus low PM (p<0.005). Redrawn.10

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and glutathione levels in alveolar macrophages and lymphocytes by diesel

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5-lipoxygenase expression in airway epithelial cells. Am J Respir Cell Mol Biol

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exercise in a high-particulate-matter environment is attenuated by montelukast.

Inhal Toxicol 2005;17:99–105. 30. Nurkiewicz TR, Porter DW, Barger M, et al. Systemic microvascular dysfunction

and infl ammation after pulmonary particulate matter exposure. Environ Health

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ice arenas. Inhal Toxicol 2003;15:237–50. 32. Mannix ET, Farber MO, Palange P, et al. Exercise-induced asthma in fi gure

skaters. Chest 1996;109:312–15. 33. Provost-Craig MA, Arbour KS, Sestili DC, et al. The incidence of exercise-

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2000;32:732–7. 35. Weiler JM, Layton T, Hunt M. Asthma in United States Olympic athletes who

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6. Rundell K W. Pulmonary function decay in women ice hockey players: is there a relationship to ice rink air quality? Inhal Toxicol 2004;16:117–23.

7. Rundell K W, Spiering BA, Evans TM, et al. Baseline lung function, exercise- induced bronchoconstriction, and asthma-like symptoms in elite women ice

hockey players. Med Sci Sports Exerc 2004;36:405–10. 8. Rundell K W, Hoffman JR, Caviston R, et al. Inhalation of ultrafi ne and

fi ne particulate matter disrupts systemic vascular function. Inhal Toxicol

2007;19:133–40. 9. Rundell K W, Slee JB, Caviston R, et al. Decreased lung function after inhalation

of ultrafi ne and fi ne particulate matter during exercise is related to decreased

total nitrate in exhaled breath condensate. Inhal Toxicol 2008;20:1–9. 10. Cutrufello PT, Rundell KW, Smoliga JM, et al. Inhaled whole exhaust

and its effect on exercise performance and vascular function. Inhal Toxicol

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Med Sci Sports Exerc 2010;42:585–91. 12. Rundell K W, Caviston R. Ultrafi ne and fi ne particulate matter inhalation

decreases exercise performance in healthy subjects. J Strength Cond Res

2008;22:2–5. 13. Chalupa DC, Morrow PE, Oberdörster G, et al. Ultrafi ne particle deposition in

subjects with asthma. Environ Health Perspect 2004;112:879–82. 14. Daigle CC, Chalupa DC, Gibb FR, et al. Ultrafi ne particle deposition in humans

during rest and exercise. Inhal Toxicol 2003;15:539–52. 15. Campen MJ, Lund AK, Doyle-Eisele ML, et al. A comparison of vascular effects

from complex and individual air pollutants indicates a role for monoxide gases and

volatile hydrocarbons. Environ Health Perspect 2010;118:921–7. 16. Larsson BM, Sehlstedt M, Grunewald J, et al. Road tunnel air pollution induces

bronchoalveolar infl ammation in healthy subjects. Eur Respir J 2007;29:699–705. 17. McCreanor J, Cullinan P, Nieuwenhuijsen MJ, et al. Respiratory effects

of exposure to diesel traffi c in persons with asthma. N Engl J Med

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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: c.rolland@rgu.ac.uk

International Journal of Obesity (2012) 36, 474– 476 & 2012 Macmillan Publishers Limited All rights reserved 0307-0565/12

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.

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2 The World Bank. World Development Indicators. CO2 emissions (kt) (Online). The World Bank: Washington, DC, 2011. Available at http://data.worldbank.org (Last updated 4 October 2010).

3 Met Office. Climate change F your essential guide. Report No.: 09/0050. Met Office: Exeter, Devon, UK, 2009.

4 Faergeman O. Climate change and preventive medicine. Eur J Cardiovasc Prev Rehabil 2007; 14: 726–729.

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11 Edwards P, Roberts I. Population adiposity and climate change. Int J Epidemiol 2009; 38: 1137–1140.

12 Turner H, Wass J. Oxford Handbook of Endocrinology and Diabetes. Oxford University Press: Oxford, 2002.

13 HM Government. The UK Low Carbon Transition Plan. 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 et al. Increased 24-h energy expenditure in type 2 diabetes. Diab Care 2004; 27: 2416–2421.

18 Wilkinson RG, Pickett KE, De Vogli R. Equality, sustainability, and quality of life. BMJ 2010; 341: c5816.

Global warming and weight loss A Gryka et al

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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

tanguera@ub.edu

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

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

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Anguera et al. Observation in Sport and Physical Activity 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;

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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

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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 specificities of observational studies in the field of sport and physical activity, it is only logical that we also critically appraise alternative options described in the scientific literature. With reference to studies based on first-person and third-person descriptions, it is important to note that the former refer to “lived experiences” linked to cognitive and mental events, while the latter refer to descriptive experiences linked to the study of other natural phenomena. Both, however, can be connected using a phenomenological approach. In an experimental setting designed to analyze processes, such as attention or memory, for example, subjects are asked to perform a specific task but while doing this, they experience what can be termed “lived content.” As this is something that can be described and analyzed, it exists. What sets our proposal apart is that we always analyze spontaneous behavior in natural settings. The subject is given no instructions, as the setting is natural, not artificial. Consequently, despite the vitality of lived experiences and the issues regarding the what, why, and how of first- person methodologies (Varela and Shear, 1999a,b), the start and end points of first- and third-person methodologies are different, although it is perfectly possible to integrate qualitative and quantitative data in both approaches if there is sufficient symmetry.

One interesting point for reflection was recently proposed by Depraz et al. (2017), who addressed the challenges associated with generating productive interaction between first-person data

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(in this case micro-phenomenological interviews) and third- person data (objective physiological data) by co-interpreting both analyses (each from the other) using an approach based on “co-validation” and “mutual constraints” (Varela, 1996). Their proposal was discussed in depth in subsequent open peer commentaries (also available in Varela, 1996). In our field, reflection on the issues they discuss could give rise to a line of research focused on the perceivable expression of spontaneous behaviors, which would influence the categorization stage of our method.

We hope that the continued development of research within systematic observation will trigger reflection and inquiry and contribute to the creation of knowledge in the field of sport and physical activity. Systematic observation has commendable strengths, but it also requires a certain sacrifice in terms of time, labor, and pursuit of scientific rigor. The strength of systematic observation is that it provides a flexible yet rigorous framework for the objective analysis of behaviors in a natural setting and context; this is the only way to study spontaneous behaviors in a natural environment. In this article, we have discussed the essential role of observational studies in this respect and highlighted the benefits of transforming rigorously annotated, sequential (ordered) qualitative data into code matrices for subsequent analysis using powerful quantitative analytical techniques. Systematic observation in its current form permits the seamless integration of qualitative and quantitative data (Sánchez-Algarra and Anguera, 2013), while meeting the requirements of multi-method research studies and harnessing the potential offered by the quantitization of qualitative data using non-standard statistical techniques and the subsequent interpretive qualitization of the quantitative results (which are largely structural) using behavior sequence analysis techniques.

Finally, we would like to express our concern that the fundamental meaning of mixed methods research, at times, has been misunderstood, leading to the publication of pseudo- studies in the form of qualitative research supplemented by some quantitative results or quantitative research adorned with a qualitative component, such as an interview or autobiographical text. We believe that the unorthodox use and understanding of the term mixed methods has generated confusion that has not been adequately addressed. The literature contains many examples of studies that the authors claimed to represent mixed methods that are not, but it also contains studies that are mixed

methods research studies but whose findings are limited by a lack of integration and/or symmetry. The difficulty of integrating qualitative and quantitative research approaches has been widely acknowledged (Fetters and Freshwater, 2015b) and is largely linked to a lack of a strong scientific culture. Achieving the symmetry between the two approaches will be a difficult process because they originate from very different traditions. We are aware of the difficulties that lie ahead in our field but we believe that we need to move forward and to assume the responsibility of providing methodological training focused precisely on these two weak points: integration and symmetry.

To conclude, we would like to acknowledge the initiative shown by Fetters and Freshwater (2015a) in calling for contributions that stimulate open reflection and dialog among researchers from multiple disciplines interested in helping to define and to conceptualize mixed methods research. We, for one, are very interested in engaging in this dialog.

AUTHOR CONTRIBUTIONS

All authors contributed to documenting, drafting and writing the manuscript, and gave their approval to the final version to be published.

FUNDING

The authors gratefully acknowledge the support of two Spanish government projects (Ministerio de Economía y Competitividad): (1) La actividad física y el deporte como potenciadores del estilo de vida saludable: Evaluación del comportamiento deportivo desde metodologías no intrusivas [Grant number DEP2015-66069-P, MINECO/FEDER, UE]; (2) Avances metodológicos y tecnológicos en el estudio observacional del comportamiento deportivo [PSI2015-71947- REDP, MINECO/FEDER, UE]. In addition, the authors thank the support of the Generalitat de Catalunya Research Group, GRUP DE RECERCA I INNOVACIÓ EN DISSENYS (GRID). Tecnología i aplicació multimedia i digital als dissenys observacionals [Grant number 2014 SGR 971]. Lastly, MA and PS-A also acknowledge the support of University of Barcelona (Vice-Chancellorship of Doctorate and Research Promotion), and OC and MC gratefully acknowledge the support of INEFC (National Institute of Physical Education of Catalonia).

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Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Analysis of relationship marketing factors for

sports centers with mixed methods research

Sheng Yen Lee Department of Leisure Sports, Kyonggi University, Suwon, South Korea

Abstract Purpose – The purpose of this paper is to study the effects of relationship marketing factors of sports centers on commitment to relationship and intention to recommend. Design/methodology/approach – A statistical survey was conducted for quantitative research, and in-depth interviews, for qualitative research, according to the mixed methods research. Findings – The results of the quantitative research show that the relationship marketing factors of bonding, facility, and price positively influence commitment to relationship. Expertise and facility positively influence intention to recommend. Finally, commitment to relationship positively influences intention to recommend. Practical implications – Sports centers could build relationships based on polite and hospitable service, and host monthly events for building rapport among members. Instructors’ expertise promotes bonding and serves as the most essential factor for intention to recommend. Sports centers must adequately introduce promotions related to rational consumption and specialized promotion. Centers that are managed too carelessly or frugally will have a highly negative impact on customer relationship and intention to recommend. Originality/value – This study aims to empirically analyze customer needs by comparing the results of in-depth interviews with customers based on the results of quantitative studies through mixed methods research. It determines the relationships between the aforementioned variables, providing practical implications through analysis of the customers’ subjective consciousness by focusing on sports facilities in order to secure competitive advantage, and thus, overcome financial difficulties. Keywords Mixed methods research, Intention to recommend, Commitment to relationship, Relationship marketing factors, Sports centre, Sports facility management Paper type Research paper

Introduction In a survey conducted in the early 2000s by the Ministry of Culture, Sports and Tourism in Korea to investigate overall participation in sports, 33.6 percent of the respondents stated that, among other types of sports facilities, the sports center was most required (Ministry of Culture, Sports and Tourism, 2003; Seok, 2007; Lee, 2010). However, as the number of sports centers has increased sharply due to growing demand from the early 2000s, an increasing number of them have also been suffering from financial problems brought on by intense competition (Kim et al., 2010). According to the 2014 sports industry statistics from the Ministry of Culture, Sports and Tourism, there are 92,293 nationwide sports businesses, an increase of 1,800 businesses compared to 2013. Among these, sports facility businesses comprised of 34,942 (37.9 percent) of the businesses in 2014; 35,314 (39 percent) in 2013; and 35,412 (42 percent) in 2012. However, recently the number of sports facility businesses has reduced significantly, although the overall sports industry has been showing an increasing trend (Ministry of Culture, Sports and Tourism, 2015; Lee, 2017).

Like other service sectors, the market for commercial sports centers needs to provide higher professional and quality services for increased customer satisfaction, since customer needs should never be ignored. Sports center managers or service providers have implemented

Asia Pacific Journal of Marketing and Logistics Vol. 30 No. 1, 2018 pp. 182-197 © Emerald Publishing Limited 1355-5855 DOI 10.1108/APJML-01-2017-0004

Received 5 January 2017 Revised 31 August 2017 13 October 2017 Accepted 13 October 2017

The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/1355-5855.htm

Conflict of interest: to the author’s knowledge, this study holds no conflicts of interest.

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piecemeal business and marketing strategies, making it even more difficult to survive competition. To survive in this competitive market, these managers must adopt a differentiated and viable marketing strategy (An, 2000; Seol, 2013).

For sport centers to secure an edge in a competitive market with poor economic prospects, they must move away from the marketing practice of attracting more consumers. They must shift their marketing paradigm toward retaining existing clients, strengthening relationships with them in order to maintain and boost revenue. Hence, relationship marketing has now attracted more attention. This form of marketing is markedly different from existing ordinary transaction marketing strategies – it considers clients as partners for future revenue generation through long-term relationships (Yang et al., 2009; Choi, 2007; Berry, 1995).

Relationship marketing strengthens existing attachment relationships. One concept that describes the level of those relationships is commitment to relationship, defined in extant literature in various ways (Yoon and Kim, 2008). As a basic human psychological propensity, it refers to the level of psychological attachment; it is a tacit oath made by service providers and users to maintain their relationships (Dwyer and Oh, 1987; Gruen et al., 2000; O’Reilly, 1986; Huang, 2015). Anderson and Weitz (1992) also stated that commitment to relationship advances a stable relationship and a willingness to risk short-term losses to maintain it. It accompanies an assurance for a sustainable relationship based on its stability. Meanwhile, according to Morgan and Hunt (1994), through commitment to relationship, service providers make efforts to maintain a binding will toward consumers, whereas consumers express a binding will toward providers. Consequently, this study examines the level of commitment to relationship as an important factor of business performance with valuable implications.

Commitment to relationship positively affects the steady revenue stream of sports centers. This claim is supported by studies that show its positive impact on sales increase and performance of service enterprises, including hotels, travel agencies, restaurant headquarters and franchises, hairdressing shops, and veterinary clinics (Kim, 2004; Lee, 2008; Cote and Lathaml, 2006; Harrison-Walker, 2001). Although researchers have explored marketing variables and commitment to relationship in various sectors, few have considered it with regard to sports centers.

Another factor that deserves attention regarding sports center marketing strategies is intention to recommend – a word-of-mouth mode of communication, separate from customer satisfaction. It constitutes informal communication that delivers the overall evaluation of personal experience. This type of communication can affect consumers’ purchasing attitudes, and thus, considerably influence their purchasing decisions. As intention to recommend is a process of providing real consumer experience in person, it exerts a powerful influence on others (Seo, 2006; Sung et al., 2002; Hwang and Kim, 1995).

There have been various studies on both relationship marketing for and selection attributes of sports centers that affect customer relationship. However, qualitative researches on variables that affect customer relationship, with respect to these centers, based on in-depth interviews, have been negligible. Therefore, to survive recession and competition, it is vital to clarify the relations between relationship marketing, commitment to relationship, and intention to recommend for stronger customer ties. We thus empirically analyze customer needs by comparing the results of in-depth customer interviews based on the results of quantitative studies, through mixed methods research. We determine the relationships between the aforementioned variables in order to provide practical implications and overcome financial difficulties. Hence, with respect to securing competitive advantages, we analyze the subjective consciousness of customers who use sports facilities.

To achieve the purpose of the present study, the following research hypotheses and corresponding models were set up (Figure 1):

H1. The relationship marketing factors of sports centers positively influence commitment to relationship.

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factors

H2. The relationship marketing factors of sports centers positively influence intention to recommend.

H3. Commitment to relationship positively influences intention to recommend.

Research method Research design We conducted a statistical survey for quantitative research and in-depth interviews for qualitative research based on the mixed methods research.

The research subjects for the quantitative analysis were consumers who used commercial sports centers. The sample includes 390 people who used five commercial sports centers in Seoul and Gyeonggi Province, from September 1, 2016 to October 31, 2016, based on the convenience sampling method. Among the 390 sampled subjects, 377 copies of the questionnaire were finally selected. In total, 13 copies that contained insincere responses or judgment errors were excluded.

For the qualitative analysis, in-depth interviews were conducted six times, once every week, from May 10, 2017 to June 14, 2017. Eight participants were selected for the interviews – five users who participated in sports at the centers, one instructor who worked at a center, and two sports business managers.

We presented in-depth discussions that focus on practical contents in order to solve our research objectives. We then compared them to the statistical results from the quantitative analysis of and findings from interviews (Lee et al., 2013).

Quantitative research method Participants and sampling method. Consumers using the services of commercial sports centers were selected as the target population of this study. In total, 390 individuals who frequented five commercial sports centers in Seoul and Gyeonggi Province, from September 1, 2016 to October 31, 2016, were extracted by convenience sampling and administered a survey. Among the sample, 13 did not provide valid answers or made errors in the questionnaire, resulting in a total sample of 377. Table I shows the general characteristics of the participants.

Survey description. To fulfill the purpose of this study, a questionnaire was employed as a research tool. Each question was rated on a five-point Likert scale, ranging from 1 for “strongly disagree” to 5 for “strongly agree.” A detailed description of the research tool is as follows. First, regarding relationship marketing, 14 questions on bonding (3), employee expertise (4), physical facility (4), and price (3), originally used by Choi (2007) in an analysis of commercial sports center relationship marketing causality model, were modified and employed in this study. Second, for commitment to relationship, the study modified and used five questions – three from Yang et al.’s (2009) study on the effects of commercial sports centers’ relationship

Commitment to relationship

Intention to recommend

Relationship marketing • Bonding

• Price

• Facility

• Expertise

Figure 1. Research model

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marketing factors on customer satisfaction, commitment to relationship, and intention of repurchase; two from Lee’s (2006) study on the effects of public sports center users’ lifestyle, customer satisfaction, and commitment on customer loyalty. Third, for intention to recommend, three questions were employed – two questions from Oh’s (2008) study on the effects of commercial sports centers’ service quality on the intention of repurchase and recommendation, and one from Kim’s (2007) study on the effects of public sports facilities’ service quality on consumers’ intention to repurchase and recommend. These methods were then modified and supplemented to better serve the purpose of this study.

Survey reliability and validity. The validity of the research tool was tested according to advice from experts. Exploratory factor analysis, confirmatory factor analysis, and reliability tests were conducted. Exploratory factor analysis of the relationship marketing questionnaire found that all questions – except for question 4 on employee expertise – recorded normal factor loading values. Confirmatory factor analysis found no abnormalities as well. On the reliability test, we acquired the Cronbach’s α values, which were within the normal range, from 0.784 to 0.848. The results of the validity and reliability tests are shown in Tables II and III and Figure 2.

Subsequently, as commitment to relationship and intention to recommend are single factors, exploratory factor analysis and reliability tests were performed. The results show no issue in factor loading values and in reliability – both factors recorded 0.835 and 0.843 reliability, respectively. Table IV shows the results of the reliability tests.

Data processing. Data were processed in SPSS 21.0 and Amos 21.0. The research model was tested through frequency, exploratory factor, and confirmatory factor analyses, as well as Cronbach’s α, correlation analysis, and structural equation modeling tests. The significance level for all the tests was set at po0.05.

Qualitative research method Research participants. For qualitative analysis, six in-depth interviews were conducted, once every Wednesday, from May 10, 2017 to June 14, 2017. Eight participants were selected for the interviews – five users who participated in sports at the sports centers, one instructor who worked at a sports center, and two sports business managers. Table V shows the general characteristics of the interviewees for qualitative research.

Variables Classifications Frequency (number of participants) %

Gender Male 215 57.0 Female 162 43.0

Age 20s 133 35.3 30s 71 18.8 40s 27 7.2 50s and above 146 38.7

Experience Less than 1 year 95 25.2 1 year to less than 3 years 128 34.0 3 years to less than 7 years 102 27.1 7 years or more 52 13.8

Frequency/week Less than 3 times 108 28.6 3 to less than 5 times 189 50.1 More than 5 times 80 21.2

Exercise time/visit Less than 1 hour 46 12.2 1 hour to less than 3 hours 252 66.8 More than 3 hours 19 15.9

Note: n ¼ 377

Table I. General participant

characteristics

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In-depth interviews for qualitative research. In-depth interviews were conducted in the form of semi-structured interviews. Variables for quantitative research were categorized and written in the form of questions for research participants. Six interviews were conducted, once every Wednesday, from May 10, 2017 to June 14, 2017. Each interview session lasted 30 minutes for each individual. All contents of the interviews were recorded with the consent of the participants.

Authenticity and ethicality of qualitative research data. Confirmation of the participants is an important ethical issue in the study, and thus, the results of the analysis were shared with the participants. To secure ethicality, the objectives, and as how the results would be used, were clearly explained to the participants. The research was conducted only after obtaining consent for participation. Finally, the interviews were signed to ensure they remained anonymous, thus protecting the privacy of the participants (Lee et al., 2013; Kim, 2016).

Results Quantitative research results Correlation analysis among variables. Correlation analysis was conducted to examine auto-correlation and correlation among the variables used in this study. The results show that correlation values were all below 0.8, indicating no issue regarding auto-correlation. Table VI shows the results of the correlation analysis.

Research model goodness-of-fit test and hypotheses testing. To test the goodness-of-fit of the research model, a structural equation modeling test was performed. As presented in Table VII, the model was proven to have an adequate goodness-of-fit with Q at 1.96, root mean square residual at 0.040, comparative fit index at 0.960, normed fit index at 0.922, and root mean square error of approximation (RMSEA) at 0.050.

Relationship marketing factors Facility Bonding Expertise Price

Facility 1 0.900 0.005 −0.002 0.027 Facility 2 0.888 −0.065 −0.009 0.032 Facility 3 0.698 0.210 0.026 −0.077 Facility 4 0.583 −0.022 −0.123 −0.238 Bonding 2 0.000 0.902 −0.012 0.104 Bonding 1 0.062 0.811 −0.019 −0.034 Bonding 3 −0.032 0.716 −0.035 −0.134 Expertise 2 0.106 −0.018 0.873 0.094 Expertise 3 −0.007 0.019 0.819 −0.022 Expertise 1 −0.089 0.057 0.794 −0.101 Price 3 0.020 0.052 0.054 0.866 Price 1 −0.021 −0.020 −0.113 0.837 Price 2 0.080 0.007 0.011 0.824 KMO and Bartlett’s test KMO ¼ 0.898, χ² ¼ 2,326.035, po0.001 Reliability 0.848 0.784 0.800 0.846

Table II. Exploratory factor analysis for relation marketing factors

Q-value RMR CFI NFI RMSEA

2.130 0.043 0.971 0.947 0.055 Notes: RMR, root mean square residual; CFI, comparative fit index; NFI, normed fit index; RMSEA, root mean square error of approximation

Table III. Goodness-of-fit test for confirmatory factor analysis of relationship marketing factors

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e1

0.53

0.67

0.47

0.60

0.58

0.54

0.54

0.65

0.60

0.55

0.65

0.86

0.64

Price 3

Price 2

Price 1

Facility 4

Facility 3

Facility 2

Facility 1

Expertise 3

Expertise 2

Expertise 1

Bonding 3

Bonding 2

Bonding 1

0.73

0.82

0.69

0.77

0.76

0.74

0.73

0.81

0.78

0.74

0.80

0.81

0.80Service Price

Available facility

Expertise

Bonding

0.65

0.50

0.53

0.62

0.72

0.60

e2

e3

e4

e5

e6

e7

e8

e9

e10

e11

e12

e13

Figure 2. Confirmatory factor

analysis of relationship marketing

factors

Commitment to relationship Commitment to relationship 4 0.835 Commitment to relationship 2 0.814 Commitment to relationship 5 0.773 Commitment to relationship 3 0.737 Commitment to relationship 1 0.730 KMO and Bartlett’s test KMO ¼ 0.827, χ² ¼ 705.030, po0.001 Reliability 0.835

Intention to recommend Intention to recommend 1 0.880 Intention to recommend 2 0.871 Intention to recommend 3 0.865 KMO and Bartlett’s test KMO ¼ 0.728, χ² ¼ 460.423, po0.001 Reliability 0.843

Table IV. Exploratory factor

analysis for commitment to relationship and

intention to recommend

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The causality relationship between variables in the research model was tested, and thus, the path coefficient between bonding and commitment to relationship was 0.390 (CR ¼ 5.202***), between employee expertise and commitment to relationship was 0.123 (CR ¼ 1.772), between facility and commitment to relationship was 0.196 (CR ¼ 2.858**), and between price and commitment to relationship was 0.293 (CR ¼ 3.642***). Meanwhile, the path coefficient between bonding and intention to recommend was 0.027 (CR ¼ 0.355), between employee expertise and intention to recommend was 0.171 (CR ¼ 2.579**), between facility and intention to recommend was 0.238 (CR ¼ 3.536***), between price and

Participant Gender Age Occupation

1. Sports center user A Female 29 Graduate student 2. Sports center user B Male 35 Self-employed 3. Sports center user C Male 41 Office worker 4. Sports center user D Female 45 Housewife 5. Sports center user E Male 55 Business owner 6. Sports center instructor F Male 36 Golf instructor 7. Sports center manager G Male 44 Sports center manager 8. Sports center manager H Male 50 Sports center manager

Table V. General characteristics of the interviewees of qualitative research

Variables Bonding Expertise Facility Price Commitment to relationship

Intention to recommend

Bonding 1 Expertise 0.523** 1 Facility 0.406** 0.445** 1 Price 0.494** 0.509** 0.611** 1 Commitment to relationship 0.609** 0.543** 0.563** 0.625** 1 Intention to recommend 0.523** 0.563** 0.624** 0.633** 0.703** 1 Note: **po0.01

Table VI. Correlation analysis among variables

Path Path coefficient SE CR Results

H1 Bonding → Commitment to relationship 0.390 0.053 5.202*** Accepted Expertise → Commitment to relationship 0.123 0.054 1.772 Rejected Facility → Commitment to relationship 0.196 0.054 2.858** Accepted Price → Commitment to relationship 0.293 0.053 3.642*** Accepted

H2 Bonding → Intention to recommend 0.027 0.072 0.355 Rejected Expertise → Intention to recommend 0.171 0.068 2.579** Accepted Facility → Intention to recommend 0.238 0.069 3.536*** Accepted Price → Intention to recommend 0.126 0.069 1.599 Rejected

H3 Commitment to relationship → Intention to recommend 0.490 0.134 4.852*** Accepted Notes: CR, critical ratio; RMR, root mean square residual; CFI, comparative fit index; NFI, normed fit index; RMSEA, root mean square error of approximation. **po0.01; ***po0.001

Table VII. Results of hypothesis tests and goodness-of- fit test

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intention to recommend was 0.126 (CR ¼ 1.599), and between commitment to relationship and intention to recommend was 0.490 (CR ¼ 4.852***). Table VII shows the results of statistical significance testing.

Qualitative research results Based on the content that affects customer relationships at sports centers, this study categorized the variables into four types, which is similar to the quantitative research on relationship marketing. Categorization for the interviews in the qualitative research was carried out based on an in-depth analysis of 17 items in the measurement tool (Lee et al., 2013; Kim, 2016) used to measure relationship marketing, with the author and two professors of sports marketing. Table VIII shows the categorization for qualitative interviews of the sub-factors of relationship marketing in sports centers.

Bonding. The most important factor to facilitate customer relationships in sports centers is various forms of activities carried out by sports centers, such as “events for building rapport among customers,” “anniversary events for customers,” “comfortable relationship with staff,” “kind and hospitable service,” “efforts to obtain customer information,” “constant sharing of information,” and “smooth communication with customers”:

It makes me feel good whenever the staff at the sports center remembers me and call me by name. This makes me think that I’ve built quite a close relationship with them. If I have a good rapport with the staff, I don’t necessarily want to move to a different sports center (Sports center user A).

I usually play golf, and I make it a point to attend the monthly golf tournaments for members if possible. This seems like a good chance for me to bond with many people. I can also check how much I’ve improved, so it also helps me keep up with my exercise (Sports center user B).

If I make friends while working out, I think I just register even if I can’t make it to the sports center often because I’m busy. And come to think of it, I become close with other members of the sports center because I share a bond with those who learn from the same instructor. I’m definitely willing to recommend this sports center to my acquaintances (Sports center user C).

A lot of sports-related information on SNS [Social Network Service] sent by the sports center is not always helpful. And emails or messages that seem too commercial rather have a negative impact.

Factors of relationship marketing Details

Bonding Events for building rapport among customers Anniversary events for customers Comfortable relationship with staff Kind and hospitable service Efforts to obtain customer information Constant sharing of information Smooth communication with customers

Expertise Expertise of instructors Expertise of staff Knowledge and experience in the relevant tasks Prompt problem-solving skills

Price Adequacy of price compared with facilities Adequacy of price compared with services Price promotions and events

Facility Safety of facilities State-of-the-art facilities Size of facilities Convenience of facilities (parking, rest, restaurants, and cafeteria)

Table VIII. Categorization for

qualitative interviews of sub-factors of

relationship marketing

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But emails or gifts sent to me on my birthday or other anniversaries make me feel closer to the sports center as if working out at the center is a big part of my life (Sports center user D).

As the manager of the sports center, I care a lot about how to build stronger ties and better relationship with our customers. This is closely related to their reentrance. And once human relations are built among customers or between customers and instructors, it becomes more likely that the customers will continue to visit the sports center, which makes it more important than other factors. Thus, planning all kinds of contests like the monthly golf tournament for members is the most fundamental event for bonding (Sports center manager G).

Based on the interviews above, sports centers could build relationships based on polite and hospitable service; monthly events, such as golf tournaments for building rapport among members; and bonding over the same instructor. As discovered in the interview with sports center manager G, holding monthly sports events is an essential factor for building relationships with customers and between instructors and customers. This factor may also help increase intention to recommend, as mentioned in the interview with sports center user C. However, e-mails or messages that seem too commercial have a negative impact according to sports center user D, which suggests that it is necessary to carefully maintain relationships with members using SNS.

Expertise. Expertise of services provided by sports centers for customers is classified into two types: expertise of sports instructors who provide sports contents and directly instruct customers, and expertise of staff that provide general services. In particular, since it is difficult to explore customer behavior toward expertise of sports instructors with a tool that measures general service quality, close observations are needed through direct interviews with customers:

Expertise is classified into “expertise of instructors,” “expertise of staff,” “knowledge and experience in the relevant tasks,” and “prompt problem-solving skills.”

I play golf, and I think the instructor’s abilities are really important in golf. The sports center is quite a distance away from my house, but my friend strongly recommended this golf instructor, so I keep coming here to learn golf despite all the inconveniences (Sports center user B).

I think golf is one sport that is most difficult to learn. I have 20 years of experience, but still, it’s really hard to play very well, and I always feel that it’s difficult to master completely. I’ve taken lessons with multiple instructors, but it’s still hard to meet a good one. I come to this sports center because it’s close to my house, but I just practice by myself without taking lessons. If I find any instructor who can give me proper lessons, I’ll definitely go there right away (Sports center user E).

Since we can get a lot of information about sports these days on the Internet or in the media, I think I have more knowledge about sports. But as I learn from the instructors at the sports center, I sometimes think that they lack theoretical expertise even though they have excellent practical skills. This often makes me reluctant to come to the center. So I stopped working out in some cases because of that (Sports center user D).

I don’t think I consider the expertise of other staff aside from the instructors at the sports center. As long as I can get proper instruction, I don’t care much about lack of facilities or unprofessional staff (Sports center user C).

I think the prompt response of the staff is most important. For example, when there’s something wrong with the teeing ground or sporting equipment, taking prompt measures or repairing it quickly will improve the overall image of the sports center (Sports center user A).

Based on the interviews above, service quality of sports centers is quite different from service attributes of hotels, restaurants, or hair salons that provide general services. According to sports center user C, as long as the user is properly instructed at the sports center, there is nothing wrong with the lack of general expertise of staff aside from the instructors.

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Moreover, B and E also mentioned that for sports that are relatively difficult to learn, such as golf, they are willing to cope with traveling a long distance from home as far as they can learn properly. They also mentioned that they would consider moving to a different sports center with a better golf instructor. This implies that recruiting good instructors in a sports center is an essential factor. The fact that good relationships are built by good instructors, and that members, such as B, would willingly travel long distances to receive instruction from a recommended instructor, show that the expertise of instructors promotes bonding. It serves as the most essential factor for intention to recommend. It seems that experienced participants in sports lay more stress on the expertise of instructors based on their multiple unsuccessful attempts at taking good lessons. Moreover, sports that are difficult to play well or learn properly tend to lay more stress on instructors as well.

Price. Contents related to price of sports centers are “adequacy of price compared with facilities,” “adequacy of price compared with service,” and “price promotions and events.” Currently, there is more supply of sports centers than demand. In this situation, price policies are relatively important. There is a need for various forms of pricing and promotions that consider consumer needs:

I’m using this sports center at quite a reasonable price. This center is actually quite expensive, but I’m using it at half price because it’s in partnership with my company. I make the payment to my company instead so that regular members cannot find out how much we pay, I think (Sports center user C).

I think it’s a good idea to offer additional discounts to members doing multiple sports at once. But it’s a shame that my center has no such system (Sports center user D).

I usually work out early in the morning because of my studies, and there aren’t a lot of people during those hours. I think holding an early-bird promotional event can be a good idea to attract more members. It will also be a great benefit for me if I can use it at a more reasonable price (Sports center user A).

Some programs include charge for facility use and the lesson fee, but while some sports need lessons, others don’t. So I think it’s rational for both customers and sports centers to come up with pricing policies for different needs (Sports center user B).

I think the most challenging thing about running a sports center is to set prices and hold promotional events of discounts. Since there are too many competitors around, I consider both promotion for rational consumption and specialized promotion. Rational promotion indicates pricing considering various needs in the consumers’ perspective, while specialized promotion is about implementing our own specialized strategy instead of considering the prices of our competitors, making an attempt at a differentiated operating method by offering higher-quality lessons and sports that are not offered by other centers. It’s necessary to adequately mix the two methods, and setting prices always puts me in a dilemma (Sports center manager H).

I use this center to take lessons even though it’s quite a distance from my house, because I was recommended to learn from this golf instructor. So, I don’t care much about the cost for practicing. I’m also participating in the intensive program that is most expensive, so I don’t think I’ll switch sport centers because of pricing (Sports center user B).

Based on the interviews above, we find that sports centers must adequately apply promotion related to rational consumption and specialized promotion, as mentioned by sports center manager H. It is necessary to formulate prices considering the diverse needs of customers from the perspective of rational consumption. At the same time, these centers must also incorporate specialized promotions that offer special, high-quality lessons, rather than considering competitor prices and developing sports items not offered by other centers. As mentioned by B, taking lessons and using the most expensive program at a center that is far from home just because a certain golf instructor was recommended has no relation to rational consumption. However, B’s satisfaction with the recommended instructor indicates

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that it has a considerable effect on commitment to relationship with sports center instructors and intention to recommend. Therefore, sports managers must consider the needs of various customers related to rational consumption, and develop specialized programs to wisely implement pricing policies.

Facility. Contents related to sports facilities are “safety of facilities,” “latest facilities,” “scale of facilities,” and “convenience of facilities, such as parking, rest, restaurants, and cafeteria”:

I think everyone prefers the latest equipment and clean facilities. As manager of a sports center, I constantly inspect the equipment for defects even if I can’t bring in the latest equipment all the time, and try to prevent all inconveniences for customers by repairing broken equipment as quickly as possible. If such equipment or goods are left unrepaired for a long time, I think it would be bad for the image of our sports center (Sports center manager G).

I love that our sports center has a famous coffee shop franchise to begin with. After working out at the center, I enjoy a nice cup of coffee in a pleasant mood with those I work out with. It’s one of the things that make me happy in life. I think a decent cafeteria and coffee shop are essential for a sports center (Sports center user D).

As a golf instructor, I think safety is the most important. I don’t think many of my customers taking lessons are sensitive about the safety. The space between each practice bays is very narrow in our golf practice facility. If someone who is not golf instructor teaches acquaintance, I think there may be very dangerous situations. It’s really important to make enough spaces between each practice bays to avoid accidents in golf practice facility (Sports center instructor F).

If there’s anyone trying to open a sports center, I’d like to tell them to secure a parking space, and check whether they have the building structure to implement the sports they basically want to manage before they make a decision. I saw many managers facing a fiasco in running a sports center without securing a parking space. And suppose the ceilings are too low to have group exercises like aerobics. Customers might not feel comfortable in that case. Also, too many pillars in the building make the space stuffy, which is also something to think about (Sports center manager H).

My previous sports center was very big and had all the latest facilities, but the center was too frugal about heating and cooling, so they rarely turned on the air conditioner in summer and kept the indoor temperature too low in winter. That’s why I had to move to a different, smaller sports center that’s even slightly farther away from home. No matter how good the facilities are, I think how the center is operated is much more important. I definitely have no intention of recommending that sports center to anyone. It’s too much (Sports center user C).

Based on the interviews above, we find that the latest equipment, large size of the center, and overall facilities are important. However, how they are operated is of greater importance. It is vital, as suggested by G, to constantly check facilities for defects and immediately repair them to avoid causing customer inconvenience. Moreover, as mentioned by C, frugality in heating and cooling, irrespective of the center’s size or number of up-to-date facilities, may drive customers away to competitors; it may have a negative effect on intention to recommend. Furthermore, aside from the basic facilities of sports centers, convenient parking is also an important factor, as mentioned by H. The interview with D on provisions for a coffee shop also points to the diversity of customer needs. Therefore, while the facilities themselves are important, how they are operated has greater importance according to the interview results. Frugal or careless management of the centers will negatively affect customer relationships and intention to recommend.

Discussion This study tested the effects of the sub-elements of relationship marketing, such as bonding, service price, employee expertise, and satisfaction with facility, on commitment to relationship, which represents the sense of unity between sports centers and their clients, and on clients’ intention to make a positive recommendation of the centers. As commitment

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to relationship and intention to recommend can have considerable influences on the performance of sports centers, the two variables warrant attention.

This study was conducted in the form of integrated research to provide practical information for sports center managers by determining the causal relations and mutual effects between the factors of sports center relationship marketing, commitment to relationships, and positive intention to recommend. Integrated research consists of determining causal relations among variables through a quantitative survey with a questionnaire, and a detailed analysis of customer thoughts through interviews focusing on categorized factors of relationship marketing for in-depth exploration. This study is relevant to previous studies based on the quantitative research result. Specifically, the qualitative research conducted has further implications.

Among numerous elements of relationship marketing, bonding, facility, and price were found to have a positive effect on commitment to relationship, while expertise and facility had a positive influence on intention to recommend. Additionally, commitment to relationship positively affected intention to recommend.

Based on these results, the following discussion points are in order. First, bonding, facility, and price were the relationship marketing elements to have a

positive impact on commitment to relationship, with bonding having the strongest positive effect. Bonding is similar to terms, such as sense of belonging or sense of relationship. In other words, it refers to how much an individual is linked to a certain group. Strong bonding can be interpreted as an individual developing a strong connection to a given sports center. Therefore, we may assume that a sense of belonging and connection to a sports center may have a positive influence on commitment to relationship. In this respect, it is necessary for sports center managers to employ more approaches toward bond building to achieve greater commitment to relationship with their clients.

We conducted interviews with research participants for analysis in order to present various bonding tactics. The analysis showed that users of sports centers develop bonds with kind and hospitable service; monthly events, such as golf tournaments, for building rapport among members; and small gatherings around instructors. Monthly events turned out to be the most important factor, as the close personal relationships built by such events contributed to positive intention to recommend the sports center to others.

Through these activities, sports centers can strengthen their bonds with customers, and these bonds, in turn, positively affect commitment to relationship, a tacit expression for continuing relationships (Baik et al., 2015; Lee, 2015; Cho, 2014).

The study also found that facility and price positively affected commitment to relationship. Facility and price are key elements of service business (Lee, 2002) – particularly price, which corresponds to expenses a sports center spends on exercise facilities, equipment, and human resources, exerts a direct influence on the relationship between a sports center and its customers (Jung and Han, 2005). It is not easy for users to acquire accurate information on how sports centers operate – physical facilities and prices allow them to evaluate sports centers (Zeithaml, 1988). Facility and price, as they are open to sports center users, can affect commitment to relationship. Thus, they are considered essential variables in relationships with customers in terms of relationship and general consumer marketing.

Interviews on price showed that promotions related to the price of sports centers can be divided into two types: promotions related to rational consumption and specialized promotion. In terms of rational consumption, it was necessary to subdivide prices and programs considering the diverse customer needs. For specialized promotion, it was important to develop new lesson items and secure high-quality instructors and contents for high-level lessons, rather than consider the prices of competitors. In particular, an interviewee mentioned that he/she would go to the center for lessons despite poor accessibility and high price if he/she were recommended an excellent instructor, which implies that promotions with instructors

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specialized in training are important. Moreover, specialized promotions also have a considerable effect on commitment to relationship with sports center instructors and positive intention to recommend.

We can consider intention to recommend as word-of-mouth information. It is a more effective marketing tool than commercial advertising, since customers tend to consider information from their acquaintances as more reliable (Lee, 2009). Positive intention to recommend is significantly affected by customer satisfaction (Son and Kim, 2011). Among relationship marketing factors, facility and expertise positively influenced intention to recommend, with facility having the strongest positive impact. In fact, facility was the only relationship marketing factor that positively changed both commitment to relationship and intention to recommend, and therefore, sports center managers should pay attention to it. Lee (2017) found that facilities provided by sports centers positively affect customer loyalty and the intention of adhering to exercise, a factor related to re-purchasing. As such, it is important for sports center management to provide details on how to use facilities and specific information on exercise equipment (Lee, 2017). Thus, we may assume that positive experience in a given facility increases customer satisfaction, and thus, lead users to make a positive recommendation. This suggests that facilities available at a sports center are key components of the intention of spreading positive word-of-mouth, which may sustain the center’s long-term performance.

Based on interviews with the participants, related to the facility, we found that, while high-quality facilities are important, how they are operated is a far vital factor. As mentioned by the participants, it is necessary to routinely manage equipment to prevent defects. This way, customers do not face inconveniences. Moreover, frugality in heating and cooling would drive customers away to competitors due to unpleasant experiences. This can be a direct cause of a highly negative effect on bonding, as well as intention to recommend.

Expertise also positively influenced intention to recommend, since consumers are affected by the teaching capabilities and work ethics of sports center employees or instructors. Lee (2017) found that sports center instructors can have a positive impact on both customer loyalty and intention of adhering to exercise, which is a factor relevant to repurchase. Prior literature also suggests that people go to sports centers with many motives. Above all, users are motivated to have fun and be active by learning a new skill (Kim and Baik, 2008). This result is supported by other research that considers users’ perception and achievement motivation, which are in line with learning a new skill. Studies conducted by Kim (2009) and Park and Jang (2007) demonstrate that sports center users’ consumer behavior is attributed to their longing for understanding new information, satisfying intellectual curiosity, acquiring various information, etc.

Interviews with the participants related to expertise showed that service quality in sports centers is very different from service attributes of hotels and restaurants that provide general services. Most sports participants lay much more stress on the importance of the education services provided by sports instructors than the specialized services provided by the general staff. In particular, the importance of the instructor was prominently emphasized for sports, such as golf, that are difficult to play well. Participants experienced in sports tended to have a desire for higher quality education services by instructors based on their multiple unsuccessful attempts at taking good lessons. Therefore, based on the interview, they were willing to move to a different sports center, even at a long distance and with expensive lesson fees, as long as they were recommended a good instructor. We found that expertise of instructors promotes bonding and serves as the most essential factor for intention to recommend.

Finally, commitment to relationship positively affected intention to recommend. Commitment to relationship is a concept that values mutual altruism and is an expression of one’s willingness to form a stable relationship and risk sacrifice in the process of relationship building (Anderson and Weitz, 1992). In other words, the willingness of sports centers and users to sustain a stable relationship positively affects intention to recommend,

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which is a highly effective marketing factor. Additionally, this result suggests that an individual’s psychological status toward relationship maintenance and retention might influence his/her intention to make a positive recommendation.

Conclusion The study found that, among the relationship marketing factors, bonding, facility, and price positively affected commitment to relationship. Expertise and facility exerted a positive impact on customer intention to make a positive recommendation. Moreover, commitment to relationship has a positive effect on intention to recommend. Among factors of relationship marketing, the facility is related to both commitment to relationship and intention to recommend, which suggests that sports center managers should focus on facility maintenance, equipment purchase, and management based on diverse customer surveys. Managers should also consider adopting measures to promote sports center group meetings for greater bonding. They must provide an expertise-building program for employees to nurture personnel who fit the current market trends. In other words, the findings of this study offer practical information on how to run sports centers, which are currently facing difficulties, by identifying factors of sports center promotion.

However, this study holds certain limitations. First, the study was conducted on a limited group of people who had been using services provided by large-scale commercial sports centers located in Korean metropolitan areas. As such, further research should cover a wider range of users. For instance, these users tend to visit facilities close to their residence frequently. However, some sports resorts, such as golf and ski resorts, typically target travelers who would not make frequent visits. In this regard, there is a need for relationship marketing studies to examine such travelers, and compare them with sports center users in order to identify characteristics and differences. Such studies could present accurate data that help better manage different types of sports facilities.

Second, this study investigated factors of relationship marketing and their effects on commitment to relationship and intention to recommend. Therefore, future research should verify causal relationships between relationship marketing factors and other variables, thereby providing practical information for sports facilities.

Finally, relationship marketing factors should be categorized from the perspective of consumers and subsequently examined. Bonding, facility, price, and expertise should be divided into sub-categories before their effects on positive intention to recommend and commitment to relationship are analyzed. For example, future studies could investigate the price range at which people begin to build an intention to make a positive recommendation or the types of activities or psychological status that influence bonding.

References

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Anderson, E. and Weitz, B. (1992), “The use of pledges to build and sustain commitment distribution channels”, Journal of Marketing Research, Vol. 29 No. 1, pp. 18-34.

Baik, S.Y., Yeo, I.S. and Jang, W.Y. (2015), “The effects of relationship commitment on repurchase and switching intention through relationship marketing in fitness center”, Korean Journal of Sports Science, Vol. 24 No. 2, pp. 635-648.

Berry, L.L. (1995), “Relationship marketing of services-growing interest, emerging perspectives”, Journal of the Academy of Marketing Science, Vol. 23 No. 4, pp. 236-245.

Cho, W.D. (2014), “The effects of relationship commitment on customer satisfaction and loyalty through relationship marketing in sports center”, Korean Journal of Sports Science, Vol. 23 No. 6, pp. 753-764.

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About the author Sheng Yen Lee received the PhD Degree in Physical Education from Kyonggi University since 2008. Sheng Yen Lee has been a Lecturer in the Department of Leisure Sports, Kyonggi University for 11 years, specializing in sports facility management, sports marketing, and golf. Sheng Yen Lee can be contacted at: hercules7@hanmail.net

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