In regard to the second and third aim of the present study, the results provide insight into the nuances of mental toughness as well as its potential as a sole personality trait. For example, the results indicated a number of moderate to strong relationships between the SMTQ, its subscales and three of the factors and seven of the traits of NEO P-IR. The three NEO P-IR factors (N, O, C) accounted for 58.2% of the variance of SMTQ Total Mental Toughness (GMT) whilst three out of the thirty NEO P-IR traits (N6, O6, C1) accounted for 66.5% of the variance of Mental Toughness (GMT). This not only indicates a level of convergence between the two measures but it may also provide an argument to counter the views that mental toughness cannot be measured through a general personality measure, and that it is a separate trait (Bull et al., 2005; Gucciardi and Gordon, 2009). In terms of its relationship with the NEO PI-R, the assumed conceptual link would be between Constancy (CS) and Conscientiousness (C) given their definitions. This was supported by the results; however, closer scrutiny suggests that only two underpinning traits within Conscientiousness load onto constancy; competence (C1) and deliberation (C6). It is peculiar given the conceptualization of constancy, why it is not more strongly related to conscientiousness especially as another logically assumed trait in achievement striving (C4) did not significantly relate to constancy either. This was not the only surprising result to emerge from the SMTQ and NEO PI-R analysis. For example, Vulnerability (N6) accounted for a significant amount of variance in SMTQ mental toughness (GMT) as well as confidence (CF) but surprisingly, not in control (CT). This suggests that confidence may be measuring aspects of control and control itself, is not wholly measuring what it purports to measure. In other words, its construct validity is brought into question. It is not transparent from the original SMTQ research whether control is intended to measure both perceived ability to cope and the ability to control emotions, despite both being cited within the research (Sheard et al., 2009; Sheard, 2010). Given its relationship with anxiety (N1) and angry hostility (N2) on NEO PI-R and lack of relationship with vulnerability (N6) it would appear to be the latter only. This confusion is thrown into further disrepute considering the initial item bank for the SMTQ was generated using an extremely limited sample utilizing small number of athletes from a small range of sports and therefore could explain the issues of validity. Gaining clarity over this is important especially if the SMTQ is to become influential concerning the strategies and interventions used to develop athletes.

### Strengths and Limitations

fpsyg-10-02259 October 3, 2019 Time: 15:51 # 7

There are strengths inherent within the study and some limitations that need to be considered for future research. Technically the use of a dual instrument approach (personality and mental toughness) was seen as important for the empirical rigor of investigating mental toughness. In addition, the sample choice of a mixed ability group moves mental toughness research away from being dominated by elite only studies, something that future research needs to continue to replicate. In terms of personality measures, the study has begun to build sportspecific norm bases for the NEO PI-R as well as continuing the ongoing validation of the SMTQ within a specific sport. However, it must be considered that the small, British and male dominated sample in the current study led to a restricted choice of statistical analyses and increased the need to adopt a cautious approach to it in order to minimize Type I errors, which may have increased the possibility of Type II errors. Additionally, as the study was conducted 3 or 15 months after the event, no baseline measures were taken prior to the event for comparative purposes and further analysis. Cross-comparisons between the two tests may have been less robust, as different populations (the tests' norm groups) were used as comparator groups, than if the same comparison group was used for both tests. Further limitations arise due to sole reliance on self-report questionnaires with no alternative forms of assessment or evaluations by third parties. This is particularly relevant to the NEO PI-R as its items are not sport-specific and therefore may not be suitable for athletes or in this case MdS populations. However, future research can aid in this as establishing separate norm bases on the NEO P-IR for specific sports will add to its utility and resultingly lead to a general sports norm base. This will be advantageous to the study and development of the characteristics that allow athletes to push themselves to extremes, including mental toughness.

## Future Directions

An examination of the results of the present study allows for the suggestion of future directions of related research. For example, future investigation should involve a comparison of athletes from different competitive levels as literature has indicated an over-reliance on elite athletes to inform mental toughness research (Golby et al., 2007; Crust, 2008; Sheard et al., 2009). Additionally by encouraging the development of sport-specific norms, general personality measures could provide a broader and more holistic understanding of athletes as well as developing a greater awareness of athletes, which is especially important in team contexts (Beauchamp et al., 2007); In fact NEO PI-R is seen to impact on other domains like orientation to people, thinking style, operational style and emotional style (Costa and McCrae, 2006; Rust and Lord, 2006; Lord, 2007). This will not only allow greater pragmatism when working with athletes to develop the characteristics that are required for them to become successful in training and competitions but relating specifically to the current research, it will be advantageous to the study and development of the characteristics that allow athletes to push themselves to extremes. Additionally, further investigation of participants' openness scores could be addressed in future research to give an insight into the differences between those who use associative or dissociative strategies during ultra-endurance events, as this is an area of contention between authors (Acevedo et al., 1992; Baker et al., 2005).

# CONCLUSION

To conclude, the MdS has provided a unique opportunity to consider the personality characteristics of ultra-endurance athletes using both the NEO PI-R and the SMTQ. The NEO PI-R appears to add insight into the MdS population's personality and mental toughness that perhaps a factor-only personality measure and the SMTQ alone could not achieve. Additionally, whilst this study has potentially added some validity to the SMTQ it has also raised some conceptual questions regarding the measure and the concept of mental toughness in general. This study has potentially supported the increasing call for sport psychology to reengage with personality research to understand how individuals deal and develop the ability to deal with the challenges they face across situations, especially competition, training and lifestyle demands (Gucciardi et al., 2008). The in-depth profile that results from completing the NEO PI-R is likely to be valuable to sport psychologists and practitioners in achieving this aim although this must coincide with the establishing of sport-specific norms through future research that acknowledges the huge variance that exists in the sporting population.

# DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request to the corresponding author.

# ETHICS STATEMENT

The studies involving human participants were reviewed and approved by London Metropolitan University. The patients/participants provided their written informed consent to participate in this study.

# AUTHOR CONTRIBUTIONS

KG designed and collected the data for the study and drafted the original manuscript. C-MR co-ordinated the preparation of the draft into a final version and checked and verified the statistical analysis. LA researched the most up to date literature on mental toughness, and added this to the manuscript. LW and JB-D acted as advisors and critical friends throughout the process.

### REFERENCES

fpsyg-10-02259 October 3, 2019 Time: 15:51 # 8

**Conflict of Interest:** KG owns and manages Performance Psychology, Zeus Ltd.

The remaining 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.

Copyright © 2019 Goddard, Roberts, Anderson, Woodford and Byron-Daniel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Muscle Strength and Flexibility in Male Marathon Runners: The Role of Age, Running Speed and Anthropometry

Pantelis Theodoros Nikolaidis1,2, Juan Del Coso3 , Thomas Rosemann4 and Beat Knechtle4,5 \*

1 Exercise Physiology Laboratory, Nikaia, Greece, 2 School of Health and Caring Sciences, University of West Attica, Athens, Greece, 3 Exercise Physiology Laboratory, Camilo José Cela University, Madrid, Spain, 4 Institute of Primary Care, University of Zurich, Zurich, Switzerland, 5 Medbase St. Gallen Am Vadianplatz, St. Gallen, Switzerland

Most studies on marathon runners have focused on physiological parameters

Edited by:

Henner Hanssen, University of Basel, Switzerland

### Reviewed by:

Mark Pfeiffer, Johannes Gutenberg University Mainz, Germany Gennaro Boccia, University of Turin, Italy

> \*Correspondence: Beat Knechtle<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Exercise Physiology, a section of the journal Frontiers in Physiology

Received: 02 July 2019 Accepted: 27 September 2019 Published: 16 October 2019

### Citation:

Nikolaidis PT, Del Coso J, Rosemann T and Knechtle B (2019) Muscle Strength and Flexibility in Male Marathon Runners: The Role of Age, Running Speed and Anthropometry. Front. Physiol. 10:1301. doi: 10.3389/fphys.2019.01301 determining performance, whereas neuromuscular aspects, such as muscle strength and flexibility, have received less attention. Thus, the aim of the present study was to examine the relationship of age, body composition, and running speed with muscle strength and flexibility of recreational marathon runners. Male marathon runners (n = 130, age 44.1 ± 8.6 years, height 176 ± 6 cm, body mass 77 ± 9 body mass index 24.7 ± 2.6 kg.m−2 , and race speed 10.29 ± 1.87 km/h) were separated into eight age groups (<30, 30–35, 55–60, >60 years). Four weeks before competing in a marathon, participants performed the sit-and-reach test (SAR), squat jumps (SJ), and countermovement jumps (CMJ), and four isometric muscle strength tests (right and left handgrip, lifting with knees extended and flexed), providing an index of overall isometric muscle strength in absolute (kg) relative to body mass values (kg.kg−1 body mass). Afterward, participants competed and finished the Athens Classic Marathon (2017), and race speed was used as an index of running performance. As an average for the whole sample, SAR was 17.6 ± 8.5 cm, SJ was 24.3 ± 4.2 cm, CMJ was 25.8 ± 4.8 cm, overall isometric muscle strength was 386 ± 59 kg in absolute values and 5.06 ± 0.78 kg/kg of body mass in relative terms. The older age groups had the lowest scores in SJ (p < 0.001, ηp 2 = 0.298) and CMJ (p < 0.001, ηp 2 = 0.304), whereas no age-related difference in SAR (p = 0.908, ηp 2 = 0.022), absolute (p = 0.622, ηp 2 = 0.042) and relative isometric muscle strength (p = 0.435, ηp 2 = 0.055) was shown. Race speed correlated moderately with relative isometric strength (r = 0.42, p < 0.001), but not with the other neuromuscular measures (r < 0.13,p > 0.130). In summary, age-related differences were shown in jumping ability, but not in flexibility and isometric muscle strength. Although these parameters - except relative strength - did not relate to running speed, they were components of health-related physical fitness. Consequently, coaches and runners should consider exercises that include stretching and strengthening in their weekly program to ensure adequate levels for all components of health-related physical fitness.

Keywords: aging, isometric muscle strength, race speed, range of motion, athlete

# INTRODUCTION

fphys-10-01301 October 14, 2019 Time: 17:0 # 2

The number of annual marathon races and finishers has increased during the last few decades (Vitti et al., 2019). Along with the increase in the number of marathoners, this has raised scientific attention to the physiological needs to complete a marathon race in amateur endurance runners, because they constitute the vast majority of finishers in races held worldwide. In this context, several studies have investigated the physiological profile of marathon runners (Del Coso et al., 2017; Salinero et al., 2017). Regarding physiological characteristics of marathon runners, the interplay of maximal oxygen uptake (VO2max), running velocity at lactate threshold, and running economy have been well studied, and they are traditionally considered as the limiting factors for endurance running performance. However, other aspects such as muscle strength and flexibility have received less attention (Takayama and Nabekura, 2018; Takayama et al., 2018) despite their relevance to the above mentioned limiting factors. Even if flexibility and muscle strength were not direct determinants of running performance in marathoners, they are considered as core components of health-related physical fitness (Milanovic et al., ´ 2015), and the characterization of these physical aspects through age might help coaches and runners to improve their training programs for marathon competition.

Despite muscle flexibility and muscle strength have been considered as key factors for running performance because of their effect on running economy (Boullosa et al., 2011; Drew et al., 2011), these variables have been studied in long-distance and marathon runners only in a few studies (Jones, 2002; Trehearn and Buresh, 2009; Brown et al., 2011; Nikolaidis et al., 2018a; Del Coso et al., 2019). For instance, isometric muscle strength, sit-and-reach test (SAR), and countermovement jump (CMJ) were tested in a study in female and male marathon runners, but the focus on this investigation was placed on the effects of α-actinin-3 deficiency in these variables (Del Coso et al., 2019). With regards to muscle flexibility, SAR has been negatively associated with running economy, an index of endurance performance (Jones, 2002; Trehearn and Buresh, 2009; Brown et al., 2011). This association indicated that reduced flexibility (SAR) might be advantageous for endurance performance because it might be an indicator of joint and muscle stiffness, variables positively related to running economy (Butler et al., 2003). An interpretation of this association might be reduced SAR, reflected in stiffer musculotendinous structures during the stretch-shortening cycle, which in turn increased storage and return of elastic energy, and consequently improved running economy (Drew et al., 2011).

The existing literature described above enhanced our knowledge on neuromuscular performance of male marathon runners, however, little information existed so far on the variation of muscle strength and flexibility by age, body composition, and running performance in this race distance. Such variations might have practical applications for fitness trainers and coaches in the context of training and testing of their athletes. In addition to their relevance for sport performance, muscle strength and flexibility as components of health-related physical fitness have been mortality predictors, and their optimal values would contribute to the prevention and treatment of lifestyle diseases (e.g., osteoporosis) (Milanovic et al., 2019 ´ ). From a health-related physical fitness perspective, flexibility was widely evaluated using SAR and muscular fitness, measured by isometric tests (e.g., handgrip and lifting) and jump tests (e.g., squat jump, SJ, and CMJ) (Heyward and Gibson, 2014; American College of Sports Medicine [ACSM], 2018). Moreover, although the beneficial role of endurance running for aerobic capacity has been well known (Milanovic et al., 2015 ´ ; Gomez-Molina et al., 2018), less information exists about the neuromuscular fitness levels of humans engaged in regular endurance training. Furthermore, the age of male marathon runners has shown large variation [e.g., 43 ± 10 years in the Berlin marathon (Nikolaidis et al., 2019), 42 ± 10 years in the New York City marathon (Nikolaidis et al., 2018b)], and it would be interesting to investigate the age-related differences in neuromuscular fitness. Therefore, the aim of the present study was to examine the relationship of age, running performance, and body composition with muscle strength and flexibility of recreational marathon runners. A secondary aim was to create norms of neuromuscular fitness that could be applied as a training tool in the evaluation of male recreational marathon runners.

### MATERIALS AND METHODS

### Study Design and Participants

A cross-sectional study design was adopted in the present study. Male marathon runners (n = 130, age 44.1 ± 8.6 years, height 176 ± 6 cm, body mass 77 ± 9 kg, body mass index 24.7 ± 2.6 kg.m−2 , and race speed 10.29 ± 1.87 km/h) were separated into eight age groups (<30, n = 7; 30–35, n = 8; 35–40, n = 25; 40–45, n = 31; 45–50, n = 30; 50–55, n = 17; 55–60, n = 6; >60 years, n = 6) and performed SAR, SJ, CMJ, and four isometric muscle strength tests (right and left handgrip, lifting with extended and bended knees), providing an index of overall isometric muscle strength in absolute (kg) and relative to body mass values (kg.kg−1 body mass). In addition, all participants in the present study finished the Athens Classic Marathon (2017) 4 weeks after the exercise testing session and race speed was used as an index of endurance running performance. Participants were recruited mostly from Athens through advertisements in social media and local sport clubs, and provided written informed consent after having been enlightened about potential risks and benefits of the study. This study has been approved by the Institutional Review Board of the Exercise Physiology Laboratory, Nikaia, Greece, and has been assigned to the ethical approval number EPL2017/7.

# Equipment and Protocols

Chronological age was estimated by a table of decimals of year (accuracy 0.01 years),considering the date (day/month/year) of exercise testing session and birthday (Ross and Marfell-Jones, 1991). A digital weighting scale (HD-351; Tanita, Arlington Heights, IL, United States) and a stadiometer (SECA, Leicester, United Kingdom) were used to measure body mass and height, respectively, with participants in minimal clothing prior to

BMI, body mass index; BF, body fat percentage; FFM, fat-free mass; CSA, total thigh muscle cross-sectional area; SAR, sit-and-reach test; HG, handgrip; SJ, squat jump; CMJ, countermovement jump. The absolute and relative sum referred to the sum of the four isometric muscle strength tests (right and left handgrip test, lifting with extended and flexed knees tests).

exercise testing. The ratio of body mass (kg) and height squared (m2 ) estimated body mass index (BMI). Skinfold thickness was measured in ten anatomical sites (cheek, wattle, chest I, triceps, subscapular, abdominal, chest II, suprailiac, thigh, and calf), and their sum was considered to estimate body fat percentage (BF) (Eston and Reilly, 2001). Fat mass (FM) was calculated as "body mass × BF/100," and fat-free mass (FFM) was "body mass−FM." Total thigh muscle cross-sectional area (CSA) was estimated from the formula "(4.68 × midthigh circumference in cm)−(2.09 × anterior thigh skinfold in mm)−80.99" (Housh et al., 1995).

TABLE 2 | Percentile values of neuromuscular fitness.

Low back and hamstring flexibility was assessed using SAR (Mayorga-Vega et al., 2014), where two trials were performed against a box with the score 15 cm corresponding to the touch of toes. That is, when the participant reached the toes using the fingers, he scored 15 cm. After flexibility, participants performed a 15 min warm-up including cycling and stretching exercises, jumping ability, and isometric muscle strength tests followed. Participants performed two squat jumps (SJ) and two CMJs in counter-balanced order (Aragon-Vargas, 2000); jump height was estimated by the flight time measured with a photocell beams system (Opto-jump, Microgate Engineering, Bolzano, Italy). Both SJs and CMJs were performed with hands stabilized on hips to prevent arm-swing. The two jump tests differed in their starting position, which was with hips and knees flexed in SJ and extended in CMJ. To evaluate isometric muscle strength, four tests were administered - right and left handgrip test, and lifting with extended and flexed knees tests; use of digital handgrip dynamometer (Heyward and Gibson, 2014), and back-and-leg digital dynamometer (Takei, Tokyo, Japan) (Ten Hoor et al., 2016) - and their sum provided an overall score of absolute and relative to body mass muscle strength. In the handgrip test, the grip was adjusted to the palm size and participants were asked to squeeze it in a standing position (Sterkowicz-Przybycien et al., 2019). In the lifting with extended knees, participants stood on the platform of back-and-leg dynamometer and pulled the hand bar across their thighs, whereas the lifting with flexed knees followed the same procedure but with different ankle (i.e. ∼135◦ instead of 180◦ ) at the knee (Heyward and Gibson, 2014). For the abovementioned exercise tests, 1 min breaks were provided between tests and within trials, and the best of two trials was recorded. During all testing procedures, participants were instructed to perform maximally.

### Statistical Analyses

All statistical analyses were performed by using IBM SPSS v.20.0 (SPSS, Chicago, IL, United States) and GraphPad Prism v. 7.0 (GraphPad Software, San Diego, CA, United States). The data were tested for normality and, thereafter, parametric statistics

BMI, body mass index; SAR, sit-and-reach test; HG, handgrip; SJ, squat jump; CMJ, countermovement jump. The absolute and relative sum referred to the sum of the four isometric muscle strength tests (right and left handgrip test, lifting with extended and flexed knees tests).

were used. Mean and standard deviation were calculated for each variable. Differences in SAR, SJ, CMJ, and isometric muscle strength among age groups were examined by one-way analysis of variance (ANOVA) and subsequent Bonferroni post hoc tests. The magnitude of the differences was tested by partial eta square, evaluated as small (0.010 < ηp 2 ≤ 0.059), medium (0.059 < ηp 2 ≤ 0.138), and large (ηp 2 > 0.138) (Cohen, 1988).  In addition, 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentile scores were calculated for each neuromuscular parameter. A multiple stepwise regression was run to predict race speed from anthropometric and neuromuscular variables. In addition, a multivariate analysis of covariance (MANCOVA) was performed with muscle strength (absolute and relative overall muscle strength), flexibility and jumping ability (SJ and CMJ) as the dependent variables, age group as the fixed factor, and race speed the covariate. Significance was set at alpha = 0.05, except in the case of MANCOVA, where alpha was corrected to 0.01 (Bonferroni correction) to account for multiple ANOVAs being run.

### RESULTS

**Table 1** depicts information about the values in all testing protocols as a whole group, including ranges. Briefly, SAR was 17.6 ± 8.5 cm, SJ was 24.3 ± 4.2 cm, CMJ was 25.8 ± 4.8 cm, absolute strength was 386 ± 59 kg and

relative strength was 5.06 ± 0.78 kg/kg of body mass. Percentile norms are presented in **Table 2**. The older age groups had the lowest scores in SJ (p < 0.001, ηp 2 = 0.298) and CMJ (p < 0.001, ηp 2 = 0.304), whereas no agerelated difference in SAR (p = 0.908, ηp 2 = 0.022), absolute (p = 0.622, ηp 2 = 0.042) and relative isometric strength (p = 0.435, ηp 2 = 0.055) was shown (**Figure 1**). Age correlated moderately with SJ (r = −0.47, p < 0.001) and CMJ (r = −0.47, p < 0.001), but not with the other neuromuscular measures (r < 0.14, p > 0.120) (**Figure 2**). Race speed correlated moderately with relative isometric strength (r = 0.42, p < 0.001), but not with the other neuromuscular measures (r < 0.13, p > 0.130).

Sit-and-reach test was not related to BF, FFM, and CSA (**Figure 3**). SJ and CMJ were negatively related with small magnitude to BF, but not to FFM and CSA. The absolute muscle strength was related directly to FFM (moderate magnitude) and CSA (small magnitude), but not to BF. The relative muscle strength was negatively related to BF (large magnitude), FFM, and CSA (small magnitude).

The results of the multiple stepwise regression showed that race speed could be predicted by BF, age, BMI, and CMJ (R 2 = 0.54) (**Table 3**). According to MANCOVA, there was a statistically significant difference of medium magnitude in neuromuscular characteristics based on participants' age adjusted for race speed (F35,482 = 2.134, p < 0.001, Wilk's 3 = 0.545, ηp 2 = 0.114). There was a statistically significant effect of age group on SJ (F7,118 = 6.196, p < 0.001, ηp 2 = 0.269) and CMJ (F7,118 = 6.448, p < 0.001, ηp 2 = 0.277), but not on SAR (F7,118 = 0.277, p = 0.962, ηp 2 = 0.016), absolute (F7,118 = 0.739, p = 0.640, ηp 2 = 0.042), and relative muscle strength (F7,118 = 0.382, p = 0.911, ηp 2 = 0.022).

### DISCUSSION

The main findings of the present study were that (a) older age groups of recreational male marathon runners had lower SJ and CMJ than their younger counterparts, (b) no difference in SAR and (absolute and relative) isometric muscle strength

FIGURE 2 | Relationship of neuromuscular fitness with age. SAR, sit-and-reach test; SJ, squat jump; CMJ, countermovement jump; HG, handgrip muscle strength; absolute and relative sum referred to the sum of four measures of isometric muscle strength (right and left HG, lifting with extended and bended knees tests); shadowed areas represented 95% confidence intervals.

BF, body fat percentage; BMI, body mass index; CMJ, countermovement jump; R, coefficient of multiple correlation; R2 , coefficient of determination; SEE, standard error of the estimate; 1R 2 , R2 change resulting from entering a new variable, a denotes variable entered in each model, b standardized beta value was presented in brackets next to variable.

was observed among age groups, (c) race speed correlated with relative isometric muscle strength, but not with the other neuromuscular measures, and (d) BF was negatively related to SJ, CMJ, and relative muscle strength.

# Comparison Among Age Groups

The lower scores in SJ and CMJ in the older compared to the younger age groups, and the absence of difference in SAR and isometric muscle strength among age groups were in agreement with the correlation analysis that identified negative relationship of SJ and CMJ - and no relationship for SAR and isometric muscle strength - with age. A negative correlation of CMJ with age was previously observed in distance runners (Michaelis et al., 2008; Nikolaidis et al., 2018a). In addition, a comparison of jumping ability, as it was reflected in jumping disciplines of athletics, among age groups showed lower performance in the older age groups than their younger peers (Kundert et al., 2019a,b). These findings together suggested a decline of SJ and CMJ with age, which should be attributed to a decline of muscle fiber type II area and fat-free mass with age (Hawkins et al., 2003). An explanation of the absence of age-related differences for SAR and isometric muscle strength, or the observation of small differences in jump performance might also be a reflection of the relatively low neuromuscular fitness of participants (Nikolaidis et al., 2016).

## Relationship Between Race Speed and Neuromuscular Fitness

The moderate correlation of race speed with relative isometric muscle strength indicated that a fast marathon runner would be characterized by high muscle strength when the role of body mass was partitioned out. It has been observed previously that fast marathon runners did not differ from their slower counterparts with regards to absolute isometric muscle strength, however, when their body mass was considered, the former runners had higher relative muscle strength than the latter ones (Salinero et al., 2017). Although the application of force in leg muscles during endurance running is far from its maximal expression, high levels of relative muscle strength might be useful for marathoners in order to reduce the exercise-induced muscle damage level developed during the race (Del Coso et al., 2013) or to increase running economy (Giovanelli et al., 2017). Because muscle strength is a trainable parameter in marathoners with multiple benefits for marathoners, especially in the amateur population, concurrent resistance and endurance training should be implemented in replacement of the traditional vision of "onlyendurance" training to increase overall marathon performance (Yamamoto et al., 2008).

# Neuromuscular Fitness and Anthropometric Characteristics

The absence of relationships between SAR and BF, FFM, and CSA was expected, since flexibility has been a musculoskeletal attribute rather than a correlate of body composition. The negative relationship of SJ and CMJ with BF was explained from the observation that an additional FM consisted extra load that muscle strength of lower limbs should overtake (Gatterer et al., 2013). With regards to isometric muscle strength, the relationship of the absolute overall score with FFM (medium magnitude) and CSA (small magnitude) was in agreement with research demonstrating the association between muscle strength and muscle CSA (Fink et al., 2017). That is, an increased absolute overall muscle strength of participants was related to increased FFM and CSA. Interestingly, this trend was reversed when overall muscle strength was expressed relative to body mass values, since muscle strength depended on both muscular and neurological properties (McKay et al., 2017). This finding highlighted the need to assess and interpret muscle strength values in both absolute and relative to body mass values (Heyward and Gibson, 2014).

# Limitations, Strength and Practical Applications

A limitation of the present study was the assessment methods of neuromuscular fitness; although popular measures of flexibility (SAR), muscle strength (isometric dynamometry), and jumping ability (SJ and CMJ) were used, caution would be needed in the consideration of methodological details to compare the findings with previous research. For instance, 8.8 cm was the SAR score of distance runners in a study, where zero was set at the toes (Jones, 2002), in contrast to the 15 cm set at the toes in the present study. Thus, to have comparable data, 15 cm should be added to the data of Jones (2002). Moreover, it was acknowledged that the existence of age groups with unequal sample sizes (most participants were in the 40–45 age group, and their number was decreasing in the younger and older groups) might be subjected to criticism from a statistical point of view. It should be highlighted that the existence of unequal sample sizes in age groups of marathon runners was ecologically valid, since it was representative of the variation in the participation rates by age group in marathon races. For instance, most male marathon runners were in the 40–44 age group in the New York City Marathon (Nikolaidis et al., 2018b) and in the Berlin marathon race (Nikolaidis et al., 2019).

In addition, the period between exercise testing session and marathon race was ∼4 weeks, and physiological characteristics could change during this period. Actually, there has been evidence that although a 3 months typical endurance running

protocol (three 60 min sessions per week) improved aerobic capacity, no change in SAR, SJ, and CMJ was observed (Milanovic´ et al., 2015). Similarly, a 2 months endurance running protocol (three ∼60 min sessions per week) improved aerobic capacity, but not SJ and CMJ (Gomez-Molina et al., 2018). Therefore, it might be assumed that neuromuscular characteristics of participants in the present study would be similar both in the exercise testing session and in the date of race.

On the other hand, the measurement of several strength variables is one of the novelties of this investigation, since it was the first study - to the best of our knowledge - presenting data on a complete battery of neuromuscular fitness tests in a large sample of marathon runners through a large age range. For practical applications, coaches and fitness trainers working with marathon runners might benefit from the novel data presented during the training and testing of their athletes.

### CONCLUSION

In summary, age-related differences were shown in jumping ability, but not in flexibility and isometric muscle strength. Although these parameters - except relative strength - did

### REFERENCES

not relate to marathon performance, they were components of health-related physical fitness. Consequently, coaches and runners should consider exercises including stretching and strengthening in their weekly program to ensure adequate levels for all components of health-related physical fitness.

### DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request to the corresponding author.

### ETHICS STATEMENT

The study has been approved by the Institutional Review Board of the Exercise Physiology Laboratory, Nikaia, Greece, and has been assigned to the ethical approval number EPL2017/7.

## AUTHOR CONTRIBUTIONS

PN performed the experiments and drafted the manuscript. JD, TR, and BK helped in drafting the final manuscript.

artistic gymnasts depending on age and preferred event. PLoS One 14:e0211533. doi: 10.1371/journal.pone.0211533

**Conflict of Interest:** 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.

Copyright © 2019 Nikolaidis, Del Coso, Rosemann and Knechtle. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Perceptions of Overuse Injury Among Swedish Ultramarathon and Marathon Runners: Cross-Sectional Study Based on the Illness Perception Questionnaire Revised (IPQ-R)

William Wickström1,2, Armin Spreco1,2, Victor Bargoria1,2,3, Fredrik Elinder1,4 , Per-Olof Hansson1,5, Örjan Dahlström1,6 and Toomas Timpka1,2 \*

1 Athletics Research Center, Linköping University, Linköping, Sweden, 2 Department of Health and Care Development, and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden, 3 Department of Orthopaedics and Rehabilitation, Moi University, Eldoret, Kenya, 4 Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden, 5 Department of Management and Engineering, Linköping University, Linköping, Sweden, 6 Department of Behavioural Sciences and Learning, Linköping University, Linköping, Sweden

Edited by: Beat Knechtle, University Hospital Zurich, Switzerland

### Reviewed by:

Ana Paula Ribeiro, University of São Paulo, Brazil Arthur Jay Siegel, McLean Hospital, United States

> \*Correspondence: Toomas Timpka<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Movement Science and Sport Psychology, a section of the journal Frontiers in Psychology

Received: 04 August 2019 Accepted: 09 October 2019 Published: 22 October 2019

### Citation:

Wickström W, Spreco A, Bargoria V, Elinder F, Hansson P-O, Dahlström Ö and Timpka T (2019) Perceptions of Overuse Injury Among Swedish Ultramarathon and Marathon Runners: Cross-Sectional Study Based on the Illness Perception Questionnaire Revised (IPQ-R). Front. Psychol. 10:2406. doi: 10.3389/fpsyg.2019.02406 Background: Long-distance runners' understandings of overuse injuries are not well known which decreases the possibilities for prevention. The common sense model (CSM) outlines that runners' perceptions of a health problem can be described using the categories identity, consequence, timeline, personal control, and cause. The aim of this study was to use the CSM to investigate perceptions of overuse injury among long-distance runners with different exercise loads.

Methods: The study used a cross-sectional design. An adapted version of the illness perception questionnaire revised (IPQ-R) derived from the CSM was used to investigate Swedish ultramarathon and marathon runners' perceptions of overuse injuries. Cluster analysis was employed for categorizing runners into high and low exercise load categories. A Principal Component Analysis was thereafter used to group variables describing injury causes. Multiple logistic regression methods were finally applied using high exercise load as endpoint variable and CSM items representing perceptions of injury identity, consequence, timeline, personal control, and causes as explanatory variables.

Results: Complete data sets were collected from 165/443 (37.2%) runners. The symptoms most commonly associated with overuse injury were pain (80.1% of the runners), stiff muscles (54.1%), and stiff joints (42.0%). Overuse injury was perceived to be characterized by the possibility of personal control (stated by 78.7% of the runners), treatability (70.4%), and that the injury context was comprehensible (69.3%). The main injury causes highlighted were runner biomechanics (stated by 78.3%), the runner's personality (72.4%), and running surface biomechanics (70.0%). Among men, a belief in that personality contributes to overuse injury increased the likelihood of belonging to the high exercise load category [Odds ratio (OR) 2.10 (95% Confidence interval (95% CI) 1.38–3.19); P = 0.001], while beliefs in that running biomechanics [OR 0.56 (95% CI

0.37–0.85); P = 0.006) and mileage (OR 0.72 (95% CI 0.54–0.96); P = 0.026] causes injury decreased the likelihood. In women, a strong perception that overuse injuries can be controlled by medical interventions decreased the likelihood of high exercise load [OR 0.68 (95% CI 0.52–0.89); P = 0.005].

Conclusion: This study indicates that recognition among long-distance runners of the association between own decisions in overuse injury causation is accentuated by increased exercise loads.

Keywords: illness perceptions, long-distance running, overuse injury, exercise load, common sense model of illness, sports psychology

### INTRODUCTION

Marathon and ultramarathon running are popular forms of exercise among women and men, and participation in running competitions covering distances longer than 100 km and with 24 h duration continues to increase (Knechtle and Nikolaidis, 2018; Esteve-Lanao et al., 2019; Waldvogel et al., 2019) It is today recognized that overuse injuries constitute a common problem in runners, and that psychological factors play a role in the injury causation (van der Worp et al., 2015; Kerr et al., 2016; Hulme et al., 2017). The opportunity to achieve personal goals has been identified as the main motivation among runners to compete at the longest distances, while runners competing at shorter distances commonly report self-esteem reasons and health-related reasons as equally important (Masters and Ogles, 1995; Ogles and Masters, 2000; Ogled and Masters, 2003). Regarding the psychological effects of long-distance running, already early quantitative research reported an increase in mental fatigue and a decrease in psychological tension, and anxiety (Hassmén and Blomstrand, 1991). These effects were longer lasting than the more short-term mood changes that follow briefer sessions of aerobic exercise (van Wilgen and Verhagen, 2012). As regards overuse injuries, the role of psychological and behavioral factors has been highlighted in qualitative studies (Reed and Ones, 2006; Jelvegård et al., 2016). The results point toward that sportspersons with experiences from this injury type are prone to describe a holistic view on the causal mechanisms, where biological, psychological, and social factors are seen to contribute. Strengthening this multi-factorial view on causation, ultramarathon runners were in a recent experimental study found to have higher cold pain tolerance and lower levels of pain-related anxiety than non-running controls (Roebuck et al., 2018b). The greatest difference in anxiety scores was seen for avoidance behavior, i.e., the runners were mentally less disposed to avoid activities associated with pain. This decreased psychological predisposition to avoid pain among the ultramarathon runners was found to partially mediate the elevated cold pain tolerance.

In order to master the overuse injury problem among long-distance runners, more knowledge of runners' own understanding of overuse injuries is needed. The common sense model (CSM) of illness (Leventhal et al., 2003) suggests that health problem perceptions can be divided into five main categories: (i) Identity refers to common symptoms of ill health and the extent these are considered to be related to the actual health problem; (ii) The consequence reflect the personal evaluation of the impact of the health problem on personal life; (iii) The timeline reflects the beliefs about the course of the health problem: acute or chronic; (iv) Personal control refers to beliefs about the possibilities for personal control and cure of the health problem; and (v) The cause reflects the beliefs about the causes of the problem. Together with emotional representations, these categories constitute a theoretical foundation for study of approaches to mastering overuse injury strategies also among long-distance runners. To enable comparative studies, Weinman et al. (1996) developed the Illness Perception Questionnaire (IPQ), which was elaborated by Moss-Morris et al. (2002) to the IPQ-R (R for revised). Hagger et al. (2005) introduced the IPQ-R to the sports setting, while van Wilgen et al. (2010) adjusted the instrument to injured sportspersons (IPQ-R-S). The internal consistency of the IPQ-R-S was reported to be adequate for all dimensions and attributions except for the attribution accident or chance.

Even though psychological and behavioral characteristics of long-distance runners have been investigated, several areas with relevance for prevention of overuse injuries among runners with different exercise loads have still not been explored. The aim of this study was to use the CSM to investigate perceptions of overuse injury among long-distance runners with different exercise loads and whether some perceptions distinguish runners with the highest loads.

### MATERIALS AND METHODS

The study was based on a cross-sectional design. It was performed as a student project at Linköping University. According to Swedish legislation, student projects are not subject to external review by research ethics boards (Etikprövningsmyndigheten, 2019). The study was planned and conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki (6th revision 2008). Informed consent was obtained in writing before interview participation, which was completely voluntary. All study data were handled without breaching the integrity of individual athletes.

### Study Population

The primary study population consisted of all runners listed in the ultramarathon category at the Swedish Athletics Association

or as members in the three running clubs in the Stockholm area specialized into ultramarathon and marathon distances. Runner listings with contact information were obtained from the Swedish Athletics Association and the running clubs. The runners were contacted and informed about the study by email.

# IPQ-R-S

The IPQ-R instrument was designed to be adapted to the population to be examined. On the basis of an existing Dutch version adapted to injured athletes (IPQ-R-S) (van Wilgen et al., 2010), a Swedish IPQ-R version (Brink and Alsén, 2017) was adjusted to measure conceptions of overuse injury among active long-distance runners. Throughout the instrument, the word "disease" was replaced by the more specific term "overuse injury" and the wording "my injury" changed to denote "overuse injuries" (in general). The adjusted version (IPQ-R-S-Overuse injury) contains eight dimensions of injury characteristics and five groupings of injury causes.

The first IPQ-R-S-Overuse injury dimension is referred to as "Identity" and asks for perceived specific symptoms and whether these are perceived to be related to overuse injuries. Question 8 was changed from a general symptom ("Red-eyes") to address a more sports-specific issue ("Too much energy" from IPQ-R-S) and question 23 reworded from "Stiff joints" to "Stiff and/or painful joints." Two additions were specifically made with regard to the long-distance running context (questions 21 "weight gain" and 24 "stiff and/or painful muscles").

The remaining seven dimensions of injury characteristics examine the perception of overuse injury by asking to what extent on a five-point scale [1 (absolutely disagree) to 5 (absolutely agree)] the athlete agrees with statements linked to acute/chronic timeline, cyclic timeline, consequences, personal control, treatability (treatment control), the context of ill-health, and emotional representation.

Perceived causes of overuse injury are examined by the 28 in section C of the IPQ-R-S Overuse injury instrument and divided into five subgroups: (i) psychological attributes (for example, the emotional state), (ii) risk factors (for example, previous injury problems), (iii) infectious or immunological causes (for example, infection of virus or bacterium), (iv) accident or coincidence, and (v) a specific subcategory of causes, in this study related to overuse injuries (for example, poor footwear).

## Collection of Data

The data collection was performed using the Briteback Survey Tool TM (Norrköping, Sweden) web-based system in January 2018. A survey was constructed that asked for basic sociodemographic information, exercise load, injury history, and data for the IPQ-R-S Overuse injury. An online version of the survey, an e-mail list, and a mailings schedule were created in the web-based system (Rönnby et al., 2018). The runners were invited to participate in the study through an email that contained study information and a link to the survey. Nonresponding runners received maximum two automatic reminders by email with 10-day intervals. Automated system-generated statistics were provided for the researchers immediately after reporting of data.

# Data Analysis

The first step of the data analysis grouped the participating runners with regard to exercise load. A principal component analysis (PCA) was performed to identify different components of the exercise load. The variables used for the analyses were running sessions per week, running hours per week, running miles per week, average long-distance training velocity, hours per week strength training, and hours per week alternative training. Thereafter a cluster analysis was carried out based on the exercise load components to create two (fixed number of clusters chosen as setting) exercise load categories (low, high). Separate cluster analyses were performed for each sex.

In the second step, the runners' perception of overuse injuries (recognition of, understanding of and perceived main causes of overuse injury) were described according to exercise load categories.

In the third step, a PCA was used to describe perceived compound causal components of overuse injury (variables used: the 28 variables of IPQ-R-S-Overuse-injury Section C).

In the fourth step, binary logistic regression was used to identify aspects associated with high exercise load. The endpoint variable used in the analysis was high exercise load (low/high, as generated from cluster analysis), while the explanatory variables included sex (only in analyses of all participants together), injury history, perceived characteristics of overuse injury (8 variables), and perceived compound causes of overuse injury (components from PCA in step 3). Simple models were first analyzed. Thereafter, all explanatory variables were included multiple models, where the non-significant variables were excluded by Wald's backward stepwise regression to create separate multiple models for women, men, and the total study population. All analyses were performed in the Statistical Package for the Social Sciences (SPSS version 23).

# RESULTS

# Study Participants

From the primary study population of 443 individuals, data were collected from 165 runners (58 women, 107 men) resulting in a response rate of 37.2%. The average age of the participants was 45.9 years (females 42.3 years, males 47.8 years) (**Table 1**). About every second runner [43.0% (females 34.0%, males 47.7%)] had suffered a significant injury the previous year (time loss from running at least 3 weeks), and 29.1% (females 37.9%, males 24.3%) had a time loss injury at the time of the study. Also about every second runner [48.6% (females 62.1%, males 41.1%)] used analgesic or anti-inflammatory medication on regular basis.

# Exercise Load

The principal component analysis based on the six exercise load variables resulted in three components; running quantity (containing the variables running sessions per week, running hours per week, and running miles per week), running speed (average long-distance training velocity), and other exercise practices (hours per week strength training and hours per week alternative training). The cluster analysis based on the three

TABLE 1 |Inductivelycreated exercise load groups (low load and high load) for female and male long-distance runners determined using cluster analysis.

components resulted in 105 athletes being allocated to the low exercise load category and 60 athletes allocated to the high load category (**Table 1**). Women clustered in the high load category ran more than the women clustered in the low load category in terms of distance (89.3 km vs. 40.6 km per week), time (10.3 h vs. 4.9 h per week), and sessions (6.9 vs. 3.9 sessions per week). Also the male runners clustered in the high load category ran more in terms of distance (82.3 km vs. 38.1 km per week), time (7.8 h vs. 4.3 h per week), and sessions (6.1 vs. 3.3 sessions per week).

## Runners' Perceptions of Overuse Injury

The recognition of overuse injury among the runners as assessed by the Identity dimension in the IPQ-R-S-Overuseinjury symptoms was diffuse (**Table 2**). The symptom most commonly reported by the runners (80.1% of the respondents) to be associated with overuse injury was pain. Other symptoms connected with overuse injury were stiff muscles (54.1%), stiff joints (42.0%), and impaired physical ability (40.0%).

The understanding of overuse injury was among the ultramarathon and marathon runners characterized by possibility to personal control, treatability, and that the injury context was comprehensible (**Table 3**). Overuse injury was to a lesser extent distinguished by emotional representations and severe consequences for the runner, and the timeline included both cyclic and chronic representation of symptoms.

The runners perceived the main causes of overuse injury to be runner biomechanics, the runner's personality, and biomechanics associated with the running surface (**Table 3**). Less dominant causes contributing to overuse injuries were coaching, exercise overload, and alcohol, smoking and lifestyle. Individual predisposition for injury and stress and worry were perceived to be less important causes.

# Compound Components Perceived to Predispose for Overuse Injury

The PCA analysis of the 28 variables in IPQ-R-S-Overuse injury section C resulted in eight causal components perceived by the runners to predispose for overuse injury (**Table 4**).

Component 1 Stress and worry describes the attributes stress and anxiety as explanations for overuse injury. The two variables that most strongly loaded on the component were family problems or concerns followed by the emotional state (depression, loneliness, anxiety, and emptiness).

Component 2 Alcohol/smoking and lifestyle describes substance use together with lifestyle as explanations of overuse injury. Alcohol and smoking were the variables with strongest loadings followed by accidents and poor general health status.

Component 3 Exercise overload describes exercise load that exceeds the individual's ability as explanation for overuse injury. The variables with strongest loadings were overtraining (for a longer continuous period) followed by overuse (on occasional occasions).

Component 4 Predisposition for overuse injury describes different forms of predisposition or susceptibility. The variables coincidence or bad luck, poor previous medical care, and heredity showed the strongest loadings.

Component 5 Coaching includes different aspects of coaching as possible explanation for overuse injury. The strongest loading was shown by poorly monitored exercise.

Component 6 Biomechanics (runner) describes various aspects of runner biomechanics. The variable with strongest loading was poor equipment (e.g., shoes) followed by poor running technique.

Component 7 Biomechanics (surface) includes different aspects of biomechanics associated with the running surface. The variable with the strongest loading was the transition between different surfaces.

Component 8 The runner's personality includes behavioral items that can explain overuse injury occurrence among longdistance runners. The correlating causal variables were own behavior followed by own personality.

## Differences in Perception Between Runners With High and Low Exercise-Load

In the multiple model for men (Nagelkerke R 2 = 0.257), a strong agreement with the statement that personality contributes to overuse injury increased the likelihood of belonging to the high exercise load category [Odds ratio (OR) 2.10 [95% Confidence interval (95% CI) 1.38–3.19]; P = 0.001], while a strong perception that running biomechanics [OR 0.56 (95% CI 0.37–0.85); P = 0.006] and mileage [OR 0.72 (95% CI 0.54– 0.96); P = 0.026] contributes to injury causation decreased the likelihood (**Table 5**). In the multiple model for women (Nagelkerke R 2 = 0.229), a strong perception that overuse injuries can be controlled by medical interventions decreased the likelihood of belonging to the higher load category [OR 0.68 (95% CI 0.52–0.89); P = 0.005]. In the multiple model for the total study group (Nagelkerke R 2 = 0.179), the likelihood of belonging to the higher exercise load category was increased by a strong agreement with that personality contributes to overuse injury [OR 1.57 (95% CI 1.16–2.12); P = 0.004] and that overuse injuries have serious consequences [OR 1.11 (95% CI 1.00–1.23); P = 0.043]. A strong perception that overload injuries can be controlled by medical interventions decreased the likelihood of belonging to the high-load category [OR 0.77 (95% CI 0.67– 0.89); P = 0.001].

# DISCUSSION

The aim of this study was to use the CSM to investigate perceptions of overuse injury among ultramarathon and marathon runners and whether some perceptions distinguish runners with the highest loads. Similar to a previous study based on the IPQ-R-S among injured athletes (van Wilgen et al., 2010), we observed that the long-distance runners associated overuse injury with a diffuse illness identity (pain was the outstanding complaint) and a high perceived manageability of the injury problem. Even though the runners related overuse injury with both chronic and cyclic timelines, a high illness coherence suggest that they still perceived they comprehended the nature of this particular threat to their health. The runners expressed trust in their possibilities

TABLE 2 | Descriptive data for the IPQ-R dimension "Identity" [mean score (standard deviation)] and frequencies of marathon and ultramarathon runners [numbers (percent)] associating IPQ-R symptoms with overuse injury displayed by runner exercise load categories and sex.

The dimension score isstandardized (to maximal score 100) using min-max normalization.

TABLE 3 | Descriptivedata for the IPQ-R dimensions outlining the perceived characteristics of overuse injury and its perceived causes displayed by exercise load category and sex.

The dimension scores arestandardized (to maximal score 100) using min-max normalization (standard deviation).

TABLE 4 | Allocation of IPQ-R-S items describing overuse injury causes (C1–C28) into preliminary components (PC1–PC8) and compound causal components (CC1–CC8).

Principal component analysis [correlations in the rotated component matrix (correlation range −1 to 1) are shown].

to manage overuse injuries and generally associated these injuries with moderate consequences in their daily lives. Accordingly, they did not associate the injury category with strong emotional manifestations.

The overuse injury causes brought to the fore by most runners were runner biomechanics, the runner's personality, and biomechanics associated with the running surface. Previous studies have observed that ultramarathon runners and runners with high exercise loads often are people with a strong drive to explore their physical and mental limits (Masters and Ogles, 1995; Ogles and Masters, 2000; Ogled and Masters, 2003). A question is whether consistent "limits-exploring" traits exist in this category of runners (Roebuck et al., 2018a), and, if so, what impact these traits have on injury predisposition. The male runners with high exercise load in this study were less convinced than those with lower loads that running techniques were decisive for the occurrence of overuse injury. Instead, the importance of personality was highlighted. Also among women, runners with a high exercise load were more prone to indirectly highlight the importance of personal responsibility in prevention of overuse injuries, as they more than runners with a lower load submitted to that possibilities are limited for secondary prevention of overuse injuries using medical interventions. These findings can be compared to previous studies among competitive runners, which have showed that elite runners report "Ignoring pain" as a main risk factor for running injury (Johansen et al., 2017) and that the gradual onset of overuse injuries leads to behavioral responses characterized by neglect of the long-term implications of the injury (Reed and Ones, 2006). Together, these observations suggest that a characteristic of ultramarathon and marathon runners with high exercise loads is that these runners are conscious of

TABLE 5 | Associations between perceptions of overuse injury and belonging to the high exercise load category among long-distance runners displayed by sex [odds ratio (95% confidence interval)].

∗1.09 (1.00–1.20) (P = 0.060). ∗∗0.89 (0.78–1.00) (P = 0.054).

their psychological and behavioral response to overuse injury symptoms, in particular pain. Of note, the notions of high exercise load and running experience are not synonymous. There may have been runners who had sustained overuse injuries when rapidly increasing their exercise load, and thereby had gained the insights about their own behavior during a short period of time.

The present findings have some interesting practical implications. The attention observed in this and previous studies to be paid by ultramarathon and experienced marathon runners to their psychological capacity and skills suggest that they are aware of the possibility of being harmed by a deficient inner self-critic. Mindfulness training is therefore an intervention that can relevant to supply to long-distance runners who increase their exercise load. Mindfulness is defined as a non-judgmental, purposeful and moment-tomoment awareness comprising consciousness, awareness, and attention (Kabat-Zinn, 1982). Mindfulness training differs from traditional psychological skills development such as thought control or cognitive reframing by that participants learn to act on situations thoughtfully and with an increased level of awareness and understanding, rather than acting emotionally or impulsively (Chiesa and Serretti, 2009; Lundqvist et al., 2018). It has been reported that a 4-week mindfulness-training intervention among recreational runners resulted in improvements of state mindfulness and trait awareness and decreases in sportrelated worries personal standards perfectionism (De Petrillo et al., 2009), while no improvements in actual running performance were found. However, a recent systematic review of the evidence for mindfulness-training approaches

in sports showed that although large effect sizes were found for improving mindfulness, flow, and performance, and lower competitive anxiety, none of the 66 studies included were rated as having a low risk of bias (Noetel et al., 2019). Further research using robust designs on mindfulness among ultramarathon and marathon runners on awareness and thoughtful management of pain and overuse injuries is therefore warranted.

This study has strengths and limitations that should be taken into consideration when interpreting the results. Studies on vulnerability in sport are generally scarce despite the topic is included in investigations of resilience and mental toughness (Sarkar and Fletcher, 2014; Uphill and Hemmings, 2016). This study used the an adapted version of the IPQ-R instrument (the IPQ-R-Overuse injury), which provides possibilities for comparisons with other populations of sportspersons suffering from overuse injuries, with perceptions of other health problems in sports, and with illness perceptions in general populations. To facilitate such comparisons, the original terminology and procedures were in the main utilized for the overuse injury adaptation. However, some notions were modified to support interpretations by sports scientists, e.g., the dimension scores were standardized. Moreover, the study population consisted of all competitive ultramarathon and marathon runners listed by the participating organizations. Although the number of invited runners (n = 443) was moderate, it represented all competitive long-distance runners in the communities involved. The overall participation rate (37.2%) is comparable to previous studies or slightly lower. Circumstances that affected the participation may have been that the data collection period comprised the preseason and long weekends (travels abroad, etc.). Nonetheless,

due to the limited number of participants, the possibility of type 1 errors occurring in the inference process should be taken into regard. It also should be taken into consideration that the study was performed among runners of Scandinavian decent and having a corresponding cultural and socioeconomic background. Generalization of the results to other populations of long-distance runners should be made with care. Finally, it should be noted that the main competition discipline (ultramarathon, marathon, etc.) was not taken into regard in the clustering of runners into high and exercise low load categories. Having the ambition to compete at ultramarathon distances may thus be associated with other psychological features than those associated with high exercise loads per se.

We conclude that the results of this study indicate that recognition among long-distance runners of the association between own decisions and tissue damage in overuse injury causation is accentuated by increased exercise loads.

### DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request to the corresponding author.

### REFERENCES

# ETHICS STATEMENT

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

# AUTHOR CONTRIBUTIONS

WW, FE, AS, VB, P-OH, ÖD, and TT conceived and designed the research project. WW and TT coordinated the study development, and made substantial contributions to drafting and writing the manuscript. FE and TT recruited the study participants. WW, ÖD, and TT were involved in data collection. AS and ÖD analyzed the data. All authors contributed to the data interpretation and provided a final approval of the version to be published. TT was the guarantor of the integrity of analysis and results.

# FUNDING

VB and TT were supported by a research grant (P2018-0194) from the Swedish Centre for Sports Research (CIF).

**Conflict of Interest:** 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.

Copyright © 2019 Wickström, Spreco, Bargoria, Elinder, Hansson, Dahlström and Timpka. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Cooper Test Provides Better Half-Marathon Performance Prediction in Recreational Runners Than Laboratory Tests

### José Ramón Alvero-Cruz1 , Elvis A. Carnero2 , Manuel Avelino Giráldez García3 , Fernando Alacid4 , Thomas Rosemann5 , Pantelis T. Nikolaidis6 and Beat Knechtle5,7 \*

1 Faculty of Medicine, Andalucía TECH, University of Málaga, Málaga, Spain, 2 Florida Hospital Sanford, Translational Research Institute for Diabetes and Metabolism, Burnham Prebys Medical Discovery Institute, Orlando, FL, United States, 3 Faculty of Sports Science and Physical Education, University of A Coruña, A Coruña, Spain, 4 Department of Education, Health Research Centre, University of Almería, Almería, Spain, 5 Institute of Primary Care, University of Zurich, Zurich, Switzerland, 6 Exercise Physiology Laboratory, Nikaia, Greece, 7 Medbase St. Gallen am Vadianplatz, St. Gallen, Switzerland

### Edited by:

Hassane Zouhal, University of Rennes 2 – Upper Brittany, France

### Reviewed by:

Claire Tourny, Université de Rouen, France Chuanye Huang, Shandong Sport University, China

> \*Correspondence: Beat Knechtle<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Exercise Physiology, a section of the journal Frontiers in Physiology

Received: 31 May 2019 Accepted: 10 October 2019 Published: 05 November 2019

### Citation:

Alvero-Cruz JR, Carnero EA, Giráldez García MA, Alacid F, Rosemann T, Nikolaidis PT and Knechtle B (2019) Cooper Test Provides Better Half-Marathon Performance Prediction in Recreational Runners Than Laboratory Tests. Front. Physiol. 10:1349. doi: 10.3389/fphys.2019.01349 This study compared the ability to predict performance in half-marathon races through physiological variables obtained in a laboratory test and performance variables obtained in the Cooper field test. Twenty-three participants (age: 41.6 ± 7.6 years, weight: 70.4 ± 8.1 kg, and height: 172.5 ± 6.3 cm) underwent body composition assessment and performed a maximum incremental graded exercise laboratory test to evaluate maximum aerobic power and associated cardiorespiratory and metabolic variables. Cooper's original protocol was performed on an athletic track and the variables recorded were covered distance, rating of perceived exertion, and maximum heart rate. The week following the Cooper test, all participants completed a half-marathon race at the maximum possible speed. The associations between the laboratory and field tests and the final time of the test were used to select the predictive variables included in a stepwise multiple regression analysis, which used the race time in the half marathon as the dependent variable and the laboratory variables or field tests as independent variables. Subsequently, a concordance analysis was carried out between the estimated and actual times through the Bland-Altman procedure. Significant correlations were found between the time in the half marathon and the distance in the Cooper test (r = −0.93; p < 0.001), body weight (r = 0.40; p < 0.04), velocity at ventilatory threshold 1, (r = −0.72; p < 0.0001), speed reached at maximum oxygen consumption (vVO2max), (r = −0.84; p < 0.0001), oxygen consumption at ventilatory threshold 2 (VO2VT2) (r = −0.79; p < 0.0001), and VO2max (r = −0.64; p < 0.05). The distance covered in the Cooper test was the best predictor of time in the half-marathon, and might predicted by the equation: Race time (min) = 201.26 – 0.03433 (Cooper test in m) (R 2 = 0.873, SEE: 3.78 min). In the laboratory model, vVO2max, and body weight presented an R 2 = 0.77, SEE 5.28 min. predicted by equation: Race time (min) = 156.7177 – 4.7194 (vVO2max) – 0.3435 (Weight). Concordance analysis showed no differences between the times predicted in the models the and actual times. The data

indicated a high predictive power of half marathon race time both from the distance in the Cooper test and vVO2max in the laboratory. However, the variable associated with the Cooper test had better predictive ability than the treadmill test variables. Finally, it is important to note that these data may only be extrapolated to recreational male runners.

Keywords: prediction equations, comparison performance methods, long-distance runners, field test, laboratory test

### INTRODUCTION

The number of half-marathon runners has increased steadily over the past ten years. As an example, in the United States the number has doubled in just a decade, reaching two million in 2013. Similarly, in Europe, it is estimated that there are around 50 million people who are regular long-distance runners (Scheerder et al., 2015). Furthermore, in Spain more than 300 half-marathon races are held annually, with an important number of runners. As a result, an increasing number of amateur runners train assiduously to finish the races and improve their personal race times. It is very helpful for them to know their ideal paces or speeds for training and competition. Accordingly, identifying these values in advance is an objective necessity, for both the athletes and their coaches.

The physiological variables related to performance have been previously described (Ramsbottom et al., 1987). In the case of long-distance runners, those variables obtained in the incremental laboratory tests, essentially maximal oxygen consumption (VO2max) and its related variables, have been very useful for observing the adaptations produced by training (Legaz Arrese et al., 2006), and to predict performance in competition (Foster, 1983). Additional laboratory tests and associated variables have been proposed that may be determinants of long-duration aerobic performance. Thus, the finish time in cross-country races has been associated with a high percentage of oxygen consumption (Loftin et al., 2007), as well as with blood lactate accumulation (Loftin et al., 2007), lactate threshold (Mujika et al., 2000), ventilatory threshold, or race economy (speed reached for a given oxygen consumption) (Amann et al., 2004; Saunders et al., 2004; Lucia et al., 2006; Støren et al., 2008; Santos-Concejero et al., 2017). However, it appears that the maximal speed achieved in the laboratory tests is the variable most closely associated with sports performance, regardless of the duration of the test (Noakes et al., 1990; Knechtle et al., 2011a; Ronconi and Alvero-Cruz, 2011).

Despite conclusions from previous research, most of the participants in these studies were elite or high-level athletes, and studies that focus specifically on the half-marathon are lacking (Tanaka et al., 1984; Weyand et al., 1994; Roecker et al., 1998). Those studies carried out in amateur runners described the following as the main predictors of final test performance: kilometers per week, number of weeks of training for each event, body mass index, resting heart rate (Campbell, 1985), anaerobic threshold and vVO2max (Roecker et al., 1998), anthropometric variables (Knechtle et al., 2011a,b, 2014), or fat percentage combined with average running speed during training (Knechtle et al., 2014). However, almost all the models used in these studies involve some form laboratory assessment, and are, therefore, hardly applicable in most of the amateur population.

The usefulness of field tests (Penry et al., 2011; Alvero-Cruz et al., 2017) for the assessment of the physiological construct of aerobic condition has shown great variability with respect to laboratory tests, although their validity in predicting the construct of final race performance has been poorly addressed in the literature. It should be noted that there is a paradox between the high reliability and low ecological validity of laboratory assessments and the low reliability and high validity of the methods used in field tests (Reilly et al., 2009; Galbraith et al., 2014; Llodio et al., 2016). The Cooper test, given its simplicity of application and low cost, has traditionally been widely used to estimate maximal oxygen consumption (Cooper, 1968), but its ability to predict constructs associated with sports performance in long-distance runners, such as half-marathon runners (Grant et al., 1995), has never been evaluated. Therefore, the objective of this study was to compare the predictive ability of halfmarathon race time between two models derived from: (a) variables obtained in treadmill tests and (b) the Cooper test.

### MATERIALS AND METHODS

### Experimental Approach to the Problem

During a single visit to the laboratory, various physiological performance variables were obtained through treadmill testing and anthropometric parameters were measured with the aim of predicting half-marathon finish time. It was hypothesized that a combination of physiological variables measured during physical effort would explain half-marathon performance. Prediction equations based on the laboratory variables that best predicted performance compared with the distance covered in the Cooper Test were subsequently developed.

### Participants

The laboratory study involved 23 amateur male athletes, with a mean age of 41.6 ± 7.4 years, with experience in training (8.3 ± 5.65 years) and long-distance races. The protocol was approved by the University Ethics Committee in accordance with the Declaration of Helsinki for human research. The participants were informed of the objectives, protocols and risks associated with the experiment, and signed a written informed consent to participate in the study.

### Experimental Design

Laboratory assessments were performed in February 2011, in a single session and the half-marathon race was held at the

beginning of March of the same year. Between 10 and 21 days before the half-marathon a treadmill tests were carried out, and 7 to 10 days before the half-marathon race, in other session the athletes performed the Cooper test. All the athletes were engaged and motivated to perform at maximal effort, using their maximum heart rate for quality control of maximal effort exerted during the test (**Figure 1**).

For collection of the independent variables (associated with the different assessments), all the participants underwent a body composition assessment using anthropometry and an incremental exercise test in the laboratory with analysis of exhaled air, on the same day and within three weeks before the race. Subsequently, they undertook a Cooper test on a track, with an analysis of blood lactate. For each assessment, both in the laboratory and in the field test, which were conducted on different days, the participants were reminded to rest the day before and to have at least one meal with a high proportion of carbohydrates the previous day (rice and pasta).

### Procedures

### Anthropometric Assessment

All measurements were conducted after a 12-h fast. Weight was measured on a SECA 813 electronic scale (Hamburg, Germany) with an accuracy of 0.1 kg, and height was measured using a wall mounted SECA 216 stadiometer (Hamburg, Germany) with an accuracy 0.1 cm. Skinfolds were measured in triplicate at the following sites: triceps, subscapular, biceps and iliac crest, computing the mean for their calculation. Percentage of body fat was estimated with the Durnin-Womersley equation (Durnin and Womersley, 1974). All measurements were collected under the standardized procedures of the International Society for Advancement in Kinanthropometry (Marfell-Jones et al., 2006). The technical error of measurement of the technician was less than 3% for skinfolds and 1% for the rest of the anthropometric measurements.

### Laboratory Test

All participants underwent a maximum incremental exercise test to determine VO2max, as well as respiratory and metabolic exchange variables such as carbon dioxide output (VCO2), endtidal oxygen tension (PetO2), end-tidal carbon dioxide tension (PetCO2), ventilation, and respiratory exchange ratio (RER). The expired gases were measured breath by breath and recorded in an Ultima gas analyzer system (MedGraphics, Saint Paul, MN, United States). The system was automatically calibrated before each test, according to the manufacturer's instructions. Heart rate was recorded using a telemetric electrocardiography device (X-Scribe, Mortara, Milwaukee, WI, United States) connected to the system. Aerobic (VT1) and anaerobic (VT2) ventilatory thresholds were determined using Skinner and McLellan (1980) methodology. The participants ran on a treadmill (Ms Medisoft 870, Medisoft, Italy) according to the following protocol: After a 10-min warm-up at 5 km/h, the test began at 6 km/h with a constant gradient of 1%, and increases of 1 km/h/min until volitional exhaustion. The test was considered maximum when respiratory exchange ratio ≥ 1.15 or there was an increase of less than 2.1 ml/kg/min in VO2 between the two stages, or when a range ± 10 beats/min of the maximum predicted heart rate was reached, without these being excluding requirements according to ACSM Guidelines for exercise testing and prescription (American College of Sports Medicine, 2014). The velocity corresponding to VO2max (vVO2max), was defined as the minimum speed at which VO2max is reached (Billat et al., 1996). All the participants received verbal encouragement from the investigators to give their maximum possible effort. The percentage with respect to the theoretical heart rate (220-age) was calculated from the heart rate values. All tests were controlled by the research team.

### Cooper Test

The Cooper test (Cooper, 1968) was performed on a 400-m synthetic athletic track with the supervision of the research team. Before the test began, a 15-min warm-up of continuous running was performed at a low-moderate pace in addition to calisthenics exercises. Subsequently, the participants carried out the classic test protocol, which consisted of covering the maximum possible distance for 12 min. Immediately after completion of the test, the distance traveled was measured by means of markers placed on the track at set intervals of 50 m. During the test, the participants used a 610 Polar monitor (Polar Electro Oy, Finland). The heart rate at the end of the Cooper test was considered the maximal heart rate achieved in the test. Additionally the perceived effort

of the trial was recorded at the end of the test using the modified Borg visual scale (Borg, 1982).

### Blood Lactate

At the end of the Cooper test and in the first minute (Cabral-Santos et al., 2017), a 0.5-µl blood sample was extracted from the earlobe for measurement of the blood lactate concentration using an enzyme electrode method (Lactate Pro LT-1710, Arkray, Japan). The objective of this analysis was to corroborate the lactate level after a steady state pace. The coefficient of variation of the analyzer used is 3%.

### Weather Conditions

The weather conditions on the day of the half-marathon were cloudy, with wet soil but no rain at the time of the test. Testing took place between 10:00 a.m and 12:00 p.m, and temperatures ranged from 14 to 17◦C with a relative humidity of 42% and wind less than 14 km/h.

### Statistical Analyses

The data are presented as means and standard deviations. Normality was analyzed using the Shapiro-Wilk test. Since all the variables were normally distributed, an association analysis between variables was performed using the Pearson correlation coefficients. Variables significantly associated with race time in the half-marathon were included in a stepwise multiple regression analysis to estimate the predictors of race time (dependent variable) from two blocks of independent variables (from the laboratory and the Cooper test). Subsequently, a concordance analysis was carried out between the values predicted with the equations, obtained from the Cooper test and the laboratory model, and the actual race times in the half marathon, using Bland-Altman procedures (Bland and Altman, 1986). The difference between the values was tested with a Student's t-test and the bias using Kendall's tau correlation coefficient. The level of significance in all cases was set at p < 0.05. The statistical analysis was performed on MedCalc Statistical Software version 19.1 (MedCalc Software bv, Ostend, Belgium, 2019)1 .

### RESULTS

The characteristics of the participants are shown in **Table 1**. All were older than 30 years of age, and their body mass index

1https://www.medcalc.org

TABLE 1 | Characteristics of the sample (n = 23).

values and fat mass percentage indicated that they did not have excess adiposity.

**Table 2** presents the data on the laboratory variables. The mean HR max at the end of the treadmill test was 181 ± 14 bpm and the heart rate of the Cooper test of 177 ± 13 bpm without significant differences (P = 0.47). Respiratory exchange ratio values at the end of exercise confirm maximal effort.

### Cooper and Blood Lactate Test

The Cooper test values generally denote a maximal effort test in relation to mean lactate values of 8.31 ± 2.87 mmol/L and the high percentage of maximum theoretical heart rate (**Table 3**).

### Half Marathon Race Time

The half-marathon race was completed by all 23 runners. The mean race time of the runners was 93.28 ± 10.28 min, range (73–117 min), (CV = 11%), at an average speed of 13.68 ± 1.57 km/h (CV = 11%).

### Bivariate Correlations

**Table 4** shows the correlation coefficients between the halfmarathon race time and the different variables. Of note are the correlations with distance covered in the Cooper test (P < 0.0001), velocity at ventilatory threshold 2 (vVT2),

TABLE 2 | Treadmill test variables.

VO2VT1, oxygen uptake at VT1; VO2VT2, oxygen uptake at VT2; HRVT1, heart rate at VT1; HRVT2, heart rate at VT2; HRMax, maximum heart rate; HRMaxLab/HRMaxTheo, percentage MHRLab/MHR theoretical; VelVT1, speed at VT1; VelVT2, speed at VT2; vVO2max, speed at maximal oxygen uptake; RERVT1, respiratory exchange ratio at VT1; RERVT2, respiratory exchange ratio at VT2; RERMax, maximal respiratory exchange ratio.

TABLE 3 | Test de Cooper variables.

HR, heart rate; HRMax, maximum heart rate (220-age).

TABLE 4 | Pearson correlation coefficients between half marathon race time and treadmill variables.

HR, heart rate; v, speed; VT, ventilatory threshold; vVO2max, speed at VO2max; r, correlation coefficient.

vVO2max (both P < 0.0001), VO2VT2 (P < 0.0001), and VO2max (P < 0.0005).

### Multiple Regression Analysis

**Table 5** shows the two half-marathon time prediction models. In the first model, the variable of the distance covered in the Cooper test with the equation: Race time (min) = 201.26 – 0.03433 (Distance covered Cooper test) and in the model derived from the treadmill assessment, the maximum speed reached in the test together with the body weight are of note with the next equation:

$$\begin{aligned} \text{Racetic} (\text{min}) &= 156.7177 \, -4 : 7194 \, (\text{vVO}\_2 \text{max}) \\ &+ 0.3435 \, (\text{Weight}). \end{aligned}$$

### Concordance Analysis

The differences between the predicted value (Cooper test model) and the actual time in the half marathon were not significant (dif = −0.08 ± 3.8 min, P = 0.91), or bias (Kendall's tau, r = −0.18) (P = 0.40), with concordance limits of −7.5 to 7.4 min (**Figure 2A**). The laboratory test model values also showed no significant differences with the actual time (diff: −0.17 ± 5.03) (P = 0.83), with concordance limits of −9.7 to 10.0 min (**Figure 2B**).

The coefficient of variation between performance subjects (CV% = 100 × SD/mean) was calculated and the mean difference between the Cooper test was 20.46 m (95% CI: −20.22–61.15) (Alvero-Cruz et al., 2017).

### DISCUSSION

To the best of our knowledge, this is the only study that has evaluated and compared the ability to predict race time in the half-marathon by means of a graded exercise test in the laboratory and a Cooper test. In this work, we have identified variables associated with race time in the half-marathon through both laboratory assessment and the Cooper test. In the literature, there were several studies on different performance determinants in middle- and long-distance runners, such as anthropometric variables (Arrese and Ostáriz, 2006; Knechtle et al., 2010, 2014; Friedrich et al., 2014), variables related to training load (Ramsbottom et al., 1987; Knechtle et al., 2011a; Balsalobre-Fernández et al., 2014) as well as physiological variables (Roecker et al., 1998; Reilly et al., 2009; Rabadán et al., 2011; Ronconi and Alvero-Cruz, 2011; Gómez-Molina et al., 2017; Støa et al., 2010), which have associations with half-marathon performance.

Our results reveal that the variables from both the laboratory and the Cooper test enable significant models to be obtained for the prediction of half-marathon race time. In addition, we found that the regression model from the distance covered in the Cooper test has a better predictive value. According to results of this study, the model developed from the distance covered in the Cooper test, would explain 87.3% of the total variance of half-marathon race time in recreational runners. The laboratory model that combines vVO2max and body weight would explain 76.95% of the total variance. The variables included in our models find partial similarities with other published studies. With respect to anthropometric variables, the skinfolds of the lower limbs, anterior thigh and medial calf are associated with performance in runners of 1500 and 10000 m (Arrese and Ostáriz, 2006). Rüst et al. (2011) found that race time in the half marathon in amateur runners was associated with BMI and average training speed. Similar results are also found in relation to BMI (Friedrich et al., 2014) and sum of six skinfolds with race time in halfmarathon (Gómez-Molina et al., 2017) in male runners. Other

R 2 , coefficient of determination; adj, adjusted; MCC, multiple correlation coefficient; SEE, standard error of the estimate; VIF, variance inflation factor.

authors have linked the fat mass percentage with performance in half- marathon (Knechtle et al., 2014).

The predictive model of this author explains only 44% of the race time, which can be explained by the wide limits of concordance in the prediction of race time (±25 min) and the great heterogeneity of the study sample. In our study, the only anthropometric variable that predicts race time is body weight.

Training load variables have traditionally been associated with performance in the half marathon. These include average training speed, previous experience, distance per week, hours per week, and daily training time (Friedrich et al., 2014). In the last of these studies, associations were found with the average training speed (r = −0.77 and r = −0.58, for women and men, respectively), with fat mass percentage and the average training speed as predictors. Despite the previous evidence, no training variables were introduced into our models, which must indicate the strong dependence of training on the variability of the test result.

The dependent variables in the laboratory prediction model explain 77% of the variance. vVO2max corresponds to around 72% and body weight to 5%. Similar aspects are those presented by Knechtle et al. (2014) who, in a recent study, found predictive models of race time in the half marathon based on body weight and average training speed as predictor variables, but explaining only 44% of the race time. The results of our research provide much more accurate equations, with a coefficient of determination of 0.873 for the equation derived from the Cooper test and 0.77 for the equation derived with body weight and vVO2max.

This group of researchers (Knechtle et al., 2014) sought to improve the prediction with new equations, introducing other independent variables into the model, such as the percentage of fat mass, obtaining an R 2 of only 0.42 for the men and 0.68 for women. These coefficients are still low compared to those of our study. These authors also failed to measure VO2max and the maximum speed reached in the maximal graded test. They additionally analyze the accuracy of the prediction model using the limits of agreement (LA) between the actual value and the value predicted with the new equation. The LA of the study are very broad, between −25.6 and 24 min, with a proportional error as the race time increases. In our study, the LA for the equation derived from the distance covered in the Cooper test are from −7.4 to 7.5 min with no proportional error (P < 0.05).

The laboratory tests allowed us to obtain multiple variables, all of them well controlled and generally very reproducible. Maximal oxygen uptake is normally a good predictor of performance in long-distance runners (Hagan et al., 1981). However, this variable was not significant in either of our prediction models. Another factor that has frequently been associated with performance in runners is the maximum speed attained (vVO2max) in the incremental exercise test in the laboratory (Roecker et al., 1998). In our study, vVO2max was also a predictor in the laboratory model. This is likely due to race intensity in the half marathon being close to maximal oxygen consumption or maximum aerobic speed. Race time in the half marathon is not always explained by the absolute value of VO2max and is often best explained by the fractional use of VO2max, corresponding to a running speed, usually of a submaximal character and therefore to a submaximal VO2 value.

Nevertheless, Williams and Nute (1983) studied the physiological demands of half-marathon runners finding similarities in the physiological values of this study, in terms of the race times and VO2max values of the athletes. This study evaluated four runners and the variables that related to performance were VO2max and estimated speed at a concentration of 4 mmol/L of lactate, although they later verified that the average lactate values were 5.65 ± 1.42 mmol/L, which would confirm values close to VO2max.

Other studies such as that by Rabadán (Rabadán et al., 2011) analyze in the laboratory the physiological determinants of middle- and long-distance runners, finding that VO2max, VO2VT2 and vVT2 are variables that characterize these athletes. The strength of variables such as VT2 is that they are very reproducible parameters and therefore very useful in predicting and evaluating changes based on training. They can also help differentiate the performance of middle- and long-distance runners. In the present study, these values were not found to be predictors of performance.

## Limitations and Strengths of the Study

The main limitation is probably the small number of male participants and only performed in a half marathon event and therefore the results would only be applied to recreational runners between 73 and 117 min.

The main strength of this study is the comparison of prediction models in which the vVO2max is found as the most frequently variable related to performance in runners (Morgan et al., 1989; Noakes et al., 1990; Muñoz et al., 2012) and an easy-to-perform field test in a training schedule.

### Practical Applications

The number of long-distance runners has grown considerably in recent years. Accordingly, it is of great interest to trainers and sports science researchers to have accurate specifications regarding training and its intensities. This study enables the determination of the competition pace since an estimate of the final race time can provide greater precision in determining the different training paces, and can be assessed as often as necessary within the process. This also helps to guide the pace of the half-marathon race in amateur runners. The main application lies in the fact that a simple field test can substitute laboratory tests, offering more accurate information for the estimation of race performance, independently of physiological determinants. This undoubtedly provides a great advantage due to the simplicity of the procedure and the low economic and time cost.

# CONCLUSION

The present study described the predictive ability of finish time in the half-marathon races using variables obtained in the Cooper test and in laboratory evaluations, with a higher predictive ability found in the former. In addition to the Cooper test being statistically more powerful than the treadmill laboratory test, its main strength is that it does not require laboratory technology and can be introduced into the daily training routine to provide a relatively valid prediction of race time.

The high predictive power of distance covered in Cooper test suggest that athletes and coaches should give attention to

control of training paces and as a tool for selecting adequate competition strategies in half-marathon. Altogether, these results may guarantee a high degree of applicability for predicting half-marathon time in recreational male runners for its great reproducibility.

### DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request to the corresponding author.

### ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the protocol used in this study was approved by the Ethics Committee of the University of Málaga (2013-EMEFYDE-005) and was in accordance with the Declaration of Helsinki. The patients/participants

### REFERENCES

provided their written informed consent to participate in this study.

### AUTHOR CONTRIBUTIONS

JA-C, MG, and EC conceived and designed the study. JA-C collected the data. JA-C, MG, EC, and FA analyzed and interpreted the data, and drafted the manuscript. JA-C, MG, EC, FA, PN, TR, and BK revised the manuscript and approved the final version.

### ACKNOWLEDGMENTS

The authors thank all the athletes and the Board of Athletics Club Guadalhorce from Álora (Málaga, Spain), and especially thank the great athlete and trainer Juan Vázquez Sánchez for his immense interest and help. The authors also thank Maria Repice for her help with the English version of the text.

male runners. Open Access J. Sport. Med. 2, 113–119. doi: 10.2147/OAJSM.S2 3027

**Conflict of Interest:** 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.

Copyright © 2019 Alvero-Cruz, Carnero, Giráldez García, Alacid, Rosemann, Nikolaidis and Knechtle. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Do "Girls Just Wanna Have Fun?" Participation Trends and Motivational Profiles of Women in the Birkebeiner Races, Norway's Ultimate Mass Participation Ski Event

### Giovanna Calogiuri1 \*, Patrick Foss Johansen1 , Alessio Rossi2 and Miranda Thurston1

1 Section for Public Health, Department of Public Health and Sport Sciences, Faculty of Social and Health Sciences, Inland Norway University of Applied Sciences, Elverum, Norway, 2 Department of Computer Science, University of Pisa, Pisa, Italy

### Edited by:

Pantelis Theodoros Nikolaidis, University of West Attica, Greece

### Reviewed by:

Martin Thomas Falk, Austrian Institute of Economic Research, Austria Kjell Hausken, University of Stavanger, Norway Martin Burtscher, University of Innsbruck, Austria

> \*Correspondence: Giovanna Calogiuri<EMAIL_ADDRESS>
### Specialty section:

This article was submitted to Movement Science and Sport Psychology, a section of the journal Frontiers in Psychology

Received: 08 August 2019 Accepted: 28 October 2019 Published: 26 November 2019

### Citation:

Calogiuri G, Johansen PF, Rossi A and Thurston M (2019) Do "Girls Just Wanna Have Fun?" Participation Trends and Motivational Profiles of Women in the Birkebeiner Races, Norway's Ultimate Mass Participation Ski Event. Front. Psychol. 10:2548. doi: 10.3389/fpsyg.2019.02548 Mass participation sporting events (MPSEs) are viewed as encouraging regular exercise in the population, but concerns have been expressed about the extent to which they are inclusive for women. This study focuses on an iconic cross-country skiing MPSE in Norway, the Birkebeiner race (BR), which includes different variants (main, Friday, half-distance, and women-only races). In order to shed light on women's participation in this specific MPSE, as well as add to the understanding of women's MPSEs participation in general, this study was set up to: (i) analyze trends in women's participation, (ii) examine the characteristics, and (iii) identify key factors characterizing the motivational profile of women in different BR races, with emphasis on the fulldistance vs. the women-only races. Entries in the different races throughout the period 1996–2018 were analyzed using an autoregressive model. Information on women's sociodemographic characteristics, sport and exercise participation, and a range of psychological variables (motives, perceptions, overall satisfaction, and future participation intention) were extracted from a market survey and analyzed using a machine learning (ML) approach (n = 1,149). Additionally, qualitative information generated through open-ended questions was analyzed thematically (n = 116). The relative prevalence of women in the main BR was generally low (< 20%). While the other variants contributed to boosting women's participation in the overall event, a future increment of women in the main BR was predicted, with women's ratings possibly matching the men's by the year 2034. Across all races, most of the women were physically active, of medium-high income, and living in the most urbanized region of Norway. Satisfaction and future participation intention were relatively high, especially among the participants in the women-only races. "Exercise goal" was the predominant participation motive. The participants in women-only races assigned greater importance to social aspects, and perceived the race as a tradition, whereas those in the fulldistance races were younger and gave more importance to performance aspects.

These findings corroborate known trends and challenges in MPSE participation, but also contribute to greater understanding in this under-researched field. Further research is needed in order to gain more knowledge on how to foster women's participation in MPSEs.

Keywords: cross-country skiing, machine learning, mass participation sport, physical activity, women's health

### INTRODUCTION

### Mass-Participation Sporting Events, a Vehicle for Health Promotion?

Mass participation sporting events (MPSEs) have been defined as sporting competitions "where the primary focus is on promoting participation and engagement rather than the significance of the sporting outcome" (Coleman and Ramchandani, 2010). In contrast with other popular sporting events that receive large coverage in the media, such as the Olympic Games and different world championships, a characteristic of MPSE is that they not only attract elite athletes but, in the spirit of "sport for all" ideals, they primarily target recreational sport practitioners. This, alongside the fact that MPSEs have experienced a large increase in popularity across the world in the last three decades, means that MPSEs have been increasingly viewed as a possible vehicle for health promotion (Murphy and Bauman, 2007; Stevinson and Hickson, 2013; Murphy et al., 2015). In particular, MPSEs can provide a motivational goal for people who intend to start exercising regularly or for those who want to enhance their exercise routines.

Some research evidence suggests that MPSEs might indeed be effective in encouraging some people to enhance their exercise habits. For example, Bowles et al. (2006) found an increased frequency of biking sessions post-event compared with pre-event among first-time participants in a mass-participation biking event in Sydney. Funk et al. (2011) argue that MPSEs might also encourage lifelong patterns of sports participation, an issue that is particularly important given the tendency for physical activity levels to decline as people age, a trend that is especially common among women (McArthur et al., 2014). Moreover, MPSEs can have a social value in that the community-oriented and celebratory nature of events can generate feelings of satisfaction and inspiration that might be important in locking people into participation (Chalip, 2006). Discussing physical activity participation more generally, Gough et al. (2018) argue that there is "a web of influences" beyond individual-level accounts that relate to the context. This suggests that the context of MPSEs and the kinds of experiences they might generate is important in understanding participation.

It should, however, be noted that to use sport as an effective vehicle for health promotion, it is essential to focus on narrowing the social gradient that characterizes sport participation. In particular, promoting sport and exercise participation in those groups that tend to be less "sporty" (e.g., women, older people, and those with low socio-economic status) has been viewed as a key strategy for improving population health (World Health Organization [WHO], 2018). In this regard, concerns about the extent to which MPSEs attract those from social groups of relevance to public health have been raised (Murphy and Bauman, 2007; Murphy et al., 2015), although it has been reported that some events, especially those characterized by less challenging trails and a less competitive atmosphere (e.g., the Women's Mini Marathon in Ireland), were more successful in attracting individuals with low or moderate levels of physical activity (Bauman et al., 2009). Notwithstanding the convergence in performance between men and women in recent years in specific events (see, e.g., Hausken, 2019), from a public health perspective offering variant events (such as shorter distance and women-only races) may be beneficial if those from underrepresented groups (such as relatively inexperienced women) are to be drawn in.

# Understanding Why People Participate in Mass-Participation Sporting Events

When considering the issue of exercise promotion, especially in relation to encouraging people to increase their physical activity levels and/or maintain high physical activity levels over time, understanding people's motives is central. Knowledge on what motivates people to start and continue exercising, or more specifically what motivates them to participate in an MPSE, can inform promotional campaigns targeting specific groups of individuals. The act of purposefully training for and participating in an MPSE can be qualified as a goal-directed behavior (Forster et al., 2007). It is important to note, however, that different participants may perform such goal-directed behavior for varying reasons (Funk et al., 2011).

In general, studies have previously shown that participation in sports tends to be driven by different motivations compared to other forms of exercise. In particular, participation in sporting activities was found to be predominantly driven by enjoyment and mastery motives, while participating in fitness activities and other exercise forms was found to be primarily driven by appearance-related motives (Frederick and Ryan, 1993; Kilpatrick et al., 2005). Moreover, participation in organized sports has been associated with sociability motives to a larger extent than, for example, participation in gym-based and nature-based exercise such as walking or running in natural environments, the latter being more strongly associated with convenience-related motives as well as the enjoyment of being in contact with nature (Calogiuri and Elliott, 2017). More specifically in the context of MPSEs, a study among road running participants in the United States identified two major tiers depicting the participants' motivational profiles (Funk et al., 2011). A first, more common tier (reflected in 95% of the study participants), consisted of a combination of motives

related to "challenge," "enjoyment," "strength and endurance," and "positive health." The second tier, less common but still largely prevalent (reflected in 75% of the participants), consisted in the combination of motives related to "competition," "weight management," "ill-health avoidance," "social affiliation," "physical appearance," and "stress management." Noticeably, both these tiers of motives were significantly related to the participants' commitment to running and future exercise intention.

In light of these findings, it is plausible to postulate that the issue of the participation motive for MPSEs is complex. Moreover, different events (and sub-events) are likely to attract participants with different performance level as well as different motivational profiles. On the other hand, individuals' motivation does not represent the only factor that explains sport participation, which is, as previously noted, often subject to a social gradient. For example, a review by Murphy et al. (2015) reports that, among MPSEs' participants, women, ethnic minorities, and people with lower previous physical activity levels are often under-represented. Moreover, a systematic review of 131 studies by Beenackers et al. (2012) found consistent evidence of the association between individuals' socio-economic status and participation in leisure-time physical activity, with the most consistent socio-economic inequalities found for vigorous activities such as sports.

# Women in Mass-Participation Sporting Events

Globally, women have lower physical activity levels than their male counterparts (World Health Organization [WHO], 2010, 2018; Hallal et al., 2012). Over the past few decades, however, women's physical activity levels in many high-income countries have increased. In United Kingdom, for example, the prevalence of women meeting the minimum recommendations for physical activity is almost equal to men's (The House of Commons [THoC], 2017) and in Norway, the prevalence of sufficiently active women is slightly higher than that of men (Norwegian Directorate of Health, 2015). While women's high levels of physical activity are generally mainly driven by activities such as walking and exercising in fitness centers, their participation in other sporting activities such as running and biking tends to be lower compared to men (THoC, Statistics Norway, s.a.; 2017). In line with this pattern, in Norway, women's participation in crosscountry skiing is relatively high, though lower than that of their male counterparts (Statistics Norway, s.a.).

In line with these trends, women tend to be under-represented in MPSEs (Murphy et al., 2015). A study of the characteristics of the approximately 10,000 participants in Sydney's annual Spring Cycle showed that less than 30% of them were women (Bowles et al., 2006). These figures are in line with a recent analysis of 2,195,588 recreational marathon runners across different countries, which found that, on average, women made up only 30% of total participants (Andersen, 2019). There are, however, some signs that MPSEs are, indeed, becoming increasingly attractive for women. For instance, the analyses of recreational marathon runners showed that in the past 10 years, the growth of women's participation was threefold compared with that of men's, i.e., 27% and 8%, respectively (Andersen, 2019). As a matter of fact, in some MPSE (especially running events in Northern America) women's participation ratings already match that of men. For example, in a survey conducted among road race participants in the United States, Funk et al. (2011) found that women made up 55% of overall participants, a finding that is in line with commercial reports (see, e.g., Running USA, 2014).

To boost women's participation specifically, many MPSEs offer women-only races, which typically take place on shorter and/or less challenging routes and are characterized by a less competitive atmosphere. Some studies suggest that womenonly races not only catalyze women's participation in MPSEs, they also may help them maintain high physical activity levels over the years (Crofts et al., 2012; McArthur et al., 2014). While the addition of women-only races might be effective in boosting women's participation in absolute terms, enhancing the inclusiveness of women in main MPSEs may also have advantages, such as contributing to reducing gender stereotypes in sport as well as stimulating even greater amounts of exercise among female participants. Moreover, these events are likely to attract women who have different characteristics, motives, and aspirations than those who would rather enter main MPSE. In particular, it is plausible to assume that women in the main MPSEs have higher levels of physical training and are more interested in their performance, as opposed to the participants in women-only races, who may attach more importance to the social context and supportive and celebratory atmosphere of the event.

To date, there is a dearth of scientific studies and academic publications specifically investigating women's participation in MPSEs, both in terms of participation trends as well as the characteristics, motives, and aspirations of the women who attend. Furthermore, little is known about how such characteristics, motives, and aspirations may differ in different race contexts, especially with respect to female participants in main MPSEs or women-only races. Furthermore, it should be noted that the issue of women's inclusion in MPSEs is complex and presents a number of challenges. For example, it is argued that this phenomenon primarily benefits women who are already active and those from more advantaged social groups, while inactive women from more disadvantaged groups (e.g., older women, those from ethnic minorities and with lower socioeconomic status) tend to remain excluded (Murphy et al., 2015). A better understanding of this complex phenomenon is needed in order to assist initiatives in fostering a culture in MPSEs that encourages the participation of women from across these under-represented groups.

### The Birkebeiner Races

In the present study, we focus on a particular MPSE, the Birkebeiner races (BRs). The BRs are an iconic Norwegian crosscountry ski (classic technique) MPSE, which takes place in the region of Oppland (Inland Norway) and registers over 10,000 participants every year. The challenging 54 km trail goes through open and forest terrains, crossing two mountains (820 and 760 m above sea level). In 2018, the main BR celebrated its 80th edition; the race was launched for the first time in 1932, and since then it has been organized annually, except in the war years

1941–1945 and few other times because of adverse meteorological conditions. Alongside the main race, the event includes different variants: the Friday, the half-distance, and the women-only races. The Friday race takes place on the exact same trail two days before the main BR, and is generally characterized by fewer participants attending. Differently, the half- (28 km) and the women-only (15and 30 km) races are characterized by shorter and less challenging (relatively to the main BR) tracks. For instance, the track of the women-only 15 km race has only one major up-hill (about 550 m above sea level).

The BRs have received some research attention, especially in relation to its economic impact in the region (Stevik, 2008) as well as in relation to perspectives in sport management (Slåtten et al., 2014), sport medicine (Myrstad et al., 2014), and traumatology (see, e.g., Butcher and Brannen, 1998). On the other hand, less attention has been given to this particular event in relation to the participants' characteristics and motives, and the existing information on this particular perspective is mainly available through market reports produced by the race organizers or popular-scientific publications. In line with international literature on MPSEs (Murphy and Bauman, 2007; Murphy et al., 2015), a 2011 survey among about 900 participants in the main BR found that most lived in larger cities, were highly educated, well trained, and of male sex; women made only 19% of total participants (Rønning and Skaare, 2011). "Health" was the most common participation motive, which is generally in line with research on motives for physical activity in the Norwegian population (Norwegian Directorate of Health, 2015) as well as other Scandinavian populations (see, e.g., Aaltonen et al., 2014). Seeing the race as a personal challenge (which indicates intrinsically regulated motivation) was also a commonly reported participation motive. Remarkably, this motive was found to be more prevalent among men compared to women.

## Purpose of the Present Study

Norway boasts relatively high sports participation rates, compared to countries beyond the Nordic region, including among women (Dalen and Holbaek-Hanssen, 2016; Green et al., 2018). This provides an interesting context to study the phenomenon of women's participation in MPSEs. Moreover, the BRs are also interesting with respect to its particular location, as the region in which it takes place (Inland Norway) has one of the worst public health profiles in Norway, including having the lowest levels of physical activity compared with the rest of the country. Specifically, in order to shed light on women's participation in the different variants of the BR event, as well as contribute knowledge of women's participation in MPSEs in general, the purpose of this study was threefold:

(motives, self- and race-perceptions, overall satisfaction, and future participation intention); and

III by triangulating findings generated through a machine learning (ML) approach and a thematic analysis, identify key factors characterizing the motivational profile of women participating in either the full-distance or the women-only races.

# MATERIALS AND METHODS

# Women's Participation Trends Design and Data

In order to analyze trends in women's participation in the different variants of the BR event (objective I), a time-series analysis of their participation rates based on entry records was undertaken. Entry records of both men and women were provided by the event organizers (Birken AS), referring to the number of participants who, in each year, signed up for the different races. Data on starters (those who actually participated in the race) were also examined but not presented in this paper as the difference between these and the starting participants was fairly constant throughout the years. For the main BR, women's entries were available for the period of 1996–2018. For the Friday, the half-distance, and the women-only races (the latter, separately for the 15 and 30 km distances), entry records were obtained from the first year the races were introduced, i.e., 2010, 2012, and 2013, respectively.

### Analyses

Women's entry records were examined both as absolute numbers and as relative prevalence of women participants with respect to the overall number of participants. Autoregressive models (Wei, 2013) were created in order to forecast future participation in the main BR. The autoregressive approach allows the prediction of an event based on a weighted sum of past values reflecting the secular trend. Three time-series were analyzed for the period 1996–2018: total participants, relative prevalence of men, and relative prevalence of women. For each time series, an exponential weighted moving average (EWMA) with 6 years span was computed in order to avoid extreme frequency values that could affect the prediction of future trends. The model was trained on n-10 elements of the time series (i.e., 1996–2008) and tested on the remaining 10 waves (i.e., 2008–2018). The accuracy of the model was assessed by computing the mean squared error (MSE) between the observed and predicted values. Finally, the year in which the relative prevalence of women in the race was predicted to be equal to that of men's was based on the autoregressive function extracted from the participation frequency recorded between 1996 and 2009.

Because of the small number of observations (i.e., waves available for the analysis), it was not possible to apply the autoregression on the other races (Friday, half-distance, and women-only races). The participation trends for Friday, the halfdistance, and the women-only races where thus only examined through descriptive statistics.

### Characteristics of Women Participating Design and Participants

In order to examine the characteristics of women participating in the different variants of the BR event (objective II), as well as identify distinctive characteristics of women participating in the full-distance vs. the women-only race (objective III), a secondary analysis of data based on a market survey conducted by the race organizers was carried out. In order to perform a trend analysis on the participants' characteristics (similar to that which was done with the participation ratings), we examined the series of surveys from previous years. Although the race organizers had been conducting market surveys for several years (to our knowledge, starting from 2012), they have significantly changed the questionnaire over time, as well as across the different events, with only relatively few items being consistent. In particular, starting from 2016, the race organizers changed the statistical agency that planned and delivered the survey, leading to major changes in the structure and quantity of items included. This particular survey wave had a high degree of consistency across the different races, with the exception of the half-distance race (for which a very different questionnaire, designed directly by the race organizers, was used). We have no knowledge of other survey waves being carried out after 2016. Based on this preliminary examination, it was decided to only include the survey wave from 2016 in our analyses.

The survey was administered online by an independent market research company (Differ Strategy Consulting, Oslo) about 1 month after completion of the races among participants in the main BR, Friday, and the women-only races. The race organizers performed a separate survey among the participants of the half-distance race, but because the questionnaire used was substantially different from the one used for the other races, this information was not included in the present study. All people registered for the different races were invited to participate in the survey. Only data from female respondents who confirmed their participation in either the main BR, the Friday, and the womenonly races were used for the analyses (overall n = 1,187, overall response rate 35%). Details about the respondents and response rates for each individual race are presented in **Table 1**. The use of the data was approved by the Norwegian Centre for Research Data (Project No. 58439).

TABLE 1 | Race characteristics and overview of women participants and respondents to a market survey in 2016a .

aBased on official records available online (http://historikk.birkebeiner.no/index2. php) cross-matched with records by Birken AS. bThe response rate was calculated on the total N of participants, who were all invited to participate in the survey. cData from the market survey on the half-distance race were not included in the analyses.

### Measures

The market survey included, among other things, a set of items that were primarily designed for commercial purposes (e.g., questions regarding the brand of equipment used and visibility of sponsors). These items were removed, while only items relative to the variables of interest for the study purpose were retained, based on the assumption that they were found in identical or fairly similar form across the different races. If necessary, the items were recoded to improve the consistency across the different races, or to better address the purpose of the study (see description of variables below). These items included three groups of variables: sociodemographic characteristics, sport and exercise participation, and psychological variables (motives for participating in the race, self-perception and perceptions of the race, overall satisfaction with the race, and future participation intention). Additionally, qualitative information (responses to open-ended questions) relative to the women's motivations and perceptions were also extracted for further analyses.

### **Sociodemographic characteristics**

This included age (measured as a continuous variable), income (1 = < 200.000 NOK, 2 = 200.– 400. NOK, 3 = 400.–600 NOK, 4 = 600.–800 NOK, 5 = > 800. NOK), and region of residence. The latter was originally measured by selecting either one of the 20 Norwegian counties or the option "abroad." These were recoded into four categories: "host region" (Inland Norway, i.e., the region that hosts the races); "adjacent, most urbanized" (i.e., Oslo and Akershus; the most densely populated region, which is well connected by road and rail to the host region); "other adjacent regions" (South-Eastern Norway, the southern parts of Central Norway, which are relatively close to the host region); "farther regions" (South-Western Norway, Western Norway, the Northern parts of central Norway, Northern Norway, and abroad).

### **Sport and exercise participation**

"Other races" was a dichotomous variable indicating whether or not the women usually participated in other sporting events, of small or large scale, either in Norway or abroad. "Ski-based exercise" was a categorical variable indicating the women's skibased exercise levels compared with the past 5 years ("Compared with 5 years ago, do you engage in ski-based exercise . . .": 1 = In smaller amounts, 2 = More or less in equal amounts; 3 = In larger amounts). "Sum of sports" provided an indication of the variety of exercise activities in which the women were planning to engage in the spring season, and was constructed based on a multiple-choice item inquiring "What will be your main exercise activity in the next months?," to which the respondents answered by selecting one or more (or none) of six listed options: running, biking, roller-ski, team-sports, strength exercise, other. The individual exercise options were included in the analyses as dummy variables (Option selected = 0; Option non-selected = 1), and an additional variable was derived by summing the number of options selected, and ranged from 0 to 6.

### **Psychological variables**

Eight different motives (e.g., "To measure myself against friends" and "I usually participate every time") were measured

as dummy variables by asking the women to report whether or not they considered each particular motive as an important reason to enter the race. The women's self-perception during the race (seven items, such as "Challenged" and "Part of a community") and perceptions of the race (seven items, such as "Nice experience" and "Nice social setting") were measured on a 1–6 scale (1 = "It suits me very little"; 6 = "It suits me very well"). The women's overall satisfaction with the race was measured using a 1–10 scale (1 = "Absolutely dissatisfied"; 10 = "Absolutely satisfied"). Future participation intention was a dummy variable with 1 corresponding to the high intention to participate again the following year (i.e., "Extremely likely") while 0 corresponded to any lower intention.

### **Qualitative data**

Qualitative information was collected through an optional openended question that allowed the participants to comment on their own motives for participating and perceptions regarding the race. This question asked: "what would you say was your main motivation for participating in the race?" Among the sample, qualitative data were available from 116 women in total (note: the open-ended items were not compulsory and respondents could choose whether or not provide more in depth information in these sections).

### Analyses

### **Sample representativeness**