International  Journal  of
Environmental Research
and Public Health
Article
Amateur Runners’ Commitment: An Analysis of
Sociodemographic and Sports Habit Proﬁles
David Parra-Camacho 1
, Manuel Alonso Dos Santos 2
and
María Huertas González-Serrano 3,*
1
Department of Physical Education and Sports, Faculty of Physical Activity and Sport Sciences,
Universitat de València, 46010 Valencia, Spain; david.parra-camacho@uv.es
2
Department of Management, Faculty of Economics and Business Administration, Universidad Católica de la
Santísima Concepción, Concepción 4090541, Chile; malonso@ucsc.cl
3
Department of Teaching and Learning of Physical Education, Plastic and Music Education,
Universidad Católica de Valencia, 46110 Valencia, Spain
*
Correspondence: mh.gonzalez@ucv.es; Tel.: +34-963-983-707
Received: 14 January 2020; Accepted: 30 January 2020; Published: 2 February 2020


Abstract: The aim of this work is to analyse the commitment to running among urban runners by
identifying groups regarding commitment to this sport and by deﬁning their sociodemographic proﬁle
and their sports habits. A sample of 1806 participants in popular urban races in the city of Valencia
was interviewed using an 11-item questionnaire on commitment to running, sociodemographic
characteristics, and sports habits. The psychometric properties of the running-commitment scale
allowed for the identiﬁcation of two factors in commitment to running: enthusiasm for running
(6 items) and aﬄiction from running (5 items). Subsequently, a cluster analysis combining hierarchical
and non-hierarchical methods was performed, identifying three groups of runners: highly committed
(n = 650), moderately committed (n = 749), and slightly committed (n = 407). Highly committed
runners positively rate all aspects of running enthusiasm (M = 4.15), while moderately committed
runners show a more neutral attitude (M = 3.41) and slightly committed runners disagree on these
aspects (M = 2.41). Both highly (M = 1.32) and moderately (M = 2.04) committed runners disagree
on the aﬄiction-related aspects of running, while slightly committed runners show a trend towards
neutrality on some aﬄiction indicators. The variables referring to age, level of studies, sports habits,
and running addiction contributed to diﬀerentiating the identiﬁed groups.
Keywords: runners; commitment to running; clusters; sports habits; running addiction; urban
runners; amateur runners
1. Introduction
Today, running has become an immensely popular pastime practised in the public sphere by
millions of recreational participants around the world. However, until the 1960s, recreational jogging
in streets, parks, or forests was considered an odd activity [1]. An example of the boom in running
that has taken place in some Spanish cities can be found in Valencia (Spain), where currently more
than 30 popular races are organized every year [2]. Valencia is a city with a strong running tradition,
with runners participating in this activity since the beginning of the 19th century [2]. Also, the number of
nonprofessional sports clubs oﬀering diﬀerent sports activities has been increasing recently in the main
European cities [3,4]. However, both nonprofessional and professional clubs have limited information
about the proﬁles of athletes based on their commitment to the activities they oﬀer. The groups of
runners are heterogeneous and have diﬀerent subcultures; they could be diﬀerentiated in the frequency
of the practice of the sport, in the motives, or in the participation in competitions [5]. For each group,
Int. J. Environ. Res. Public Health 2020, 17, 925; doi:10.3390/ijerph17030925
www.mdpi.com/journal/ijerph
Int. J. Environ. Res. Public Health 2020, 17, 925
2 of 19
sports associations and clubs and governmental or nonproﬁt organizations can design communications,
activities, and services speciﬁcally targeted at them. Commitment could be a segmentation variable to
obtain diﬀerent groups of subjects with diﬀerent motivations, activities, and interests.
Commitment is a psychological construct that, from a sporting point of view, represents the desire
and willingness to continue practising a sport [6,7]. When associated with positive factors, such as the
intrinsic enjoyment of the activity itself; opportunities to participate successfully; personal investments
of time, money, and experience in the sport; and social pressures from parents, coaches, peers, etc.,
commitment increases in tandem with increases in these factors [8]. However, when associated with
negative factors, such as alternatives for successful participation in other attractive sports, commitment
decreases [8].
Another concept that has been associated with or related to this construct in previous research
on commitment to running is the notion of a positive addiction to running, deﬁned by Glasser [9]
as enjoyment that increases mental strength and, when lost, produces some type of physical or
psychological discomfort [10]. This concept is opposed to that of a negative addiction to continuous
running, which can dominate the runner’s life and can produce the unwanted eﬀects of overtraining
syndrome [11]: fatigue, decreased performance, and mood disorders.
Some studies have observed among endurance sports participants a relationship between
commitment to and dependence on exercise [12]. Endurance athletes usually train a considerable
number of hours, which some studies have found to have a positive correlation with addiction to
running among marathon runners [13,14]. However, most research indicates that commitment to
running and negative addiction to running are two diﬀerent concepts predicted by diﬀerent variables
and that, although there is a high correlation between them, the two phenomena do not necessarily
appear together [15–19].
In this regard, Pargman [20] built on these concepts (positive and negative addiction to running)
to describe the existence of two types of runners: addict-dependent runners, who have a positive
addiction towards continuous running and perceive joy and happiness in running (if they do not
do it, they feel bad), and committed-dedicated runners, who have a negative addiction towards
continuous running and a broader, rational, and pragmatic intellectual component that makes them
give continuous running a very high priority in their lives without necessarily liking it.
Among the ﬁrst contributions on commitment to running, Carmack and Martens [21],
who developed and validated the unidimensional commitment to running (CR) scale, equate
commitment to running with the concept of positive addiction to continuous running. Subsequently,
several studies have used this scale to analyse the characteristics of runners and to learn about their
sociodemographic proﬁle and sports habits. Carmack and Martens [21], and Joseph and Robbins [22]
explained that the variables that contributed to increasing the commitment to running were related to
the time spent on training (number of days of training/week and minutes/training), while Thornton
and Scott [23] also highlighted distance covered in kilometres as a predictor variable. Improved
personal performance among marathon runners was an important factor in increasing the commitment
to running for this proﬁle of runners, while amateur runners emphasized the intrinsic enjoyment of
the activity or improved health [24].
In the Spanish context, several investigations have also been carried out among Spanish runners on
the psychological variables related to motivation, commitment, and addiction to running [5,19,25–27].
In this regard, it is important to highlight the validation of the Carmack and Martens scale [21]
in Spanish marathon runners by Ruiz-Juan and Zarauz [25], who retained 11 indicators from the
12 proposed on the original unidimensional scale that allowed for evaluation of the commitment of
the runners. Several studies have analysed the relationships between training variables and sports
habits and the commitment and addiction to running in marathon runners [18,19,28]. In addition,
the motivation of amateur Spanish runners when participating in this physical activity has also been
analysed in various studies [5,26,27].
Int. J. Environ. Res. Public Health 2020, 17, 925
3 of 19
However, studies that segment runners into groups with diﬀerent behaviours towards running
are not abundant, and most of the contributions in this regard have focused on identifying groups or
on classifying runners according to their motivation to practice this physical activity [5,29,30] rather
than according to their commitment to running.
Therefore, numerous contributions that analyse diverse psychological variables such as runners’
motivation [17,20,26,27,30–35], addiction [15,19,28,36], or commitment [12,19,21–23,25,37] to running
have been made in the study of the running phenomenon. However, many of these studies have
focused on long-distance runners, such as half-marathon and marathon runners [19,28,31]. There are
few contributions that focus on Spanish urban runners who participate in short-distance races [5,26,27]
and that classify runners into clusters according to their commitment to running, identifying
groups with diﬀerent sociodemographic characteristics and sports habits. Research on short-distance
(versus long-distance) races allows researchers to study a larger universe because more and more
people practice sports for health and social reasons but are less concerned about winning long-distance
races [38].
This paper analyses the commitment to running among runners who participated in urban races
of less than 10 km. These data provide information on the behaviour of this type of urban sportspeople
in the context of Spanish short-distance runners. Spain is a country with an established running
practice: the percentage of the population who practice running is 30.4% [39].
The aim of this paper is to analyse the commitment to running among urban runners by identifying
groups with a greater or lesser commitment to this sport. In addition, the proﬁle of these groups is
deﬁned to determine their sociodemographic characteristics and to be able to analyse their sports habits.
2. Materials and Methods
2.1. Sample, Procedures, and Questionnaire
In this study, a sample of 1806 runners who participated in the city of Valencia’s urban circuit of
popular races during 2015 were interviewed. The circuit has 10 races. The total number of runners in
the circuit was 50,038 recurring runners (M = 5004). The sample size used is at least 95% conﬁdence
(5% margin of error). The questionnaires were collected after the end of this race circuit during
the month of January 2016 by using an online format. This research used a self-supplied online
questionnaire because of the diﬃculty of obtaining valid responses among active or tired athletes
immediately after the race.
The questionnaire was made up of three sections of questions. The ﬁrst included questions of a
sociodemographic nature referring to age, gender, occupation, level of studies, and income.
The second section contained questions referring to the sports habits of the participants: number
of years they have been running, weekly frequency with which they run, preferred distance when
participating in a popular race, aﬃliation with a club, whether the club is federated, with whom they
usually run, the distance they usually run weekly, the athletic level they consider themselves to have as
runners, the number of long-distance races ﬁnished (half marathons and marathons), future intentions
regarding participation in urban races, and three items on addiction to running. The three indicators on
the intentions of amateur runners were adapted from the scale on behavioural intentions by Zeithaml,
Berry, and Parasuraman [40], whereas the three indicators on running addiction were extracted from
the Spanish validation by Zarauz and Ruiz-Juan [36] of the scale on running addiction. The adaptation
of the scale consisted in translating and adapting the scales to the race under research. The indicators
on behavioural intention of amateur runners showed adequate reliability (Cronbach alpha = 0.91,
Composite Reliability = 0.92; Average Variance Extracted = 0.79). The indicators of both future
intentions and running addiction were assessed with a ﬁve-point Likert scale with the following
response options: 1 = strongly disagree, 2 = disagree, 3 = neither disagree nor agree, 4 = agree,
and 5 = strongly agree.
Int. J. Environ. Res. Public Health 2020, 17, 925
4 of 19
The third section includes indicators related to the participants’ commitment to running.
The Ruiz-Juan and Zarauz scale [25] validated in the Spanish context in marathon runners was
used to collect participants’ perceptions of their own commitment to running. This scale is composed
of 11 indicators evaluated by the interviewees through a Likert scale with the same response options as
those of future intentions and running addiction.
For this type of research, it was not necessary to get approval from the Committee of Ethics of the
university where this study was carried out (University of Valencia). According to the Committee of
Ethics and Human Research from this university, it is not necessary to get approval to carry out an
opinion survey about a topic or issue, professional status, or satisfaction with certain matters.
However, it is obligatory to include a preamble in the survey with presented information about
the project (theme and purpose), the beneﬁts that the information collected by the survey may provide,
the willingness of the participation, and the anonymous treatment of data (Data Protection Law). It is
also compulsory to indicate a contact person to ask for further information and to put one paragraph
in which the survey respondent voluntarily accepts participation in the research and gives consent
tacitly when responding to the survey. Thus, following these guidelines indicated by the Committee of
Ethics and Human Research from the University of Valencia to develop this sort of research, all this
information was added at the beginning of the questionnaire.
2.2. Statistical Analysis
First, the psychometric properties of the running commitment scale were tested on the sample
under study by performing an exploratory factor analysis (EFA) and a conﬁrmatory factor analysis
(CFA). The EFA was performed with the FACTOR program following the recommendations of
Lloret-Segura et al. [41] using the maximum likelihood (ML) method and oblimin direct rotation.
To determine the number of factors, the optimal implementation of the parallel analysis procedure was
used [42]. The model ﬁt was observed using the root mean square of the residuals (RMSR) coeﬃcient
as well as the goodness-of-ﬁt index (GFI) proposed by Tanaka and Huba [43]. The RMSR should be less
than 0.05 [44], and the GFI value should be less than 0.95 [45]. Other indicators that were taken into
account were the generalized G-H index (>0.80) to analyse the replicability of the factors derived from
the EFA [46]. The measures for sample adequation of Kaiser Meyer Olkin (KMO) were also observed,
as was Bartlett’s sphericity test. Items with factorial loads below 0.40 or above this value on two or
more factors were eliminated before the next EFA was carried out.
After applying the EFA, a CFA was performed on the factorial solution derived from the EFA
using robust maximum likelihood estimation (MLR) with the aim of correcting the possible absence of
multivariant normality using statistics such as the χ2 of Satorra-Bentler [47]. Thus, for the evaluation of
global ﬁt, diﬀerent goodness-of-ﬁt indices recommended in the literature [48], such as the signiﬁcance
of the chi-squared value and its robust correction oﬀered by Satorra-Bentler (S-B χ2) [49], were used.
In addition, other coeﬃcients that allowed for testing the adequacy of the proposed models, such
as the ratio of χ2 and its degrees of freedom (χ2/df) [50], were calculated, with the acceptable values
being less than ﬁve [51]. However, these indices are aﬀected by the sample size, so the standardized
root mean square residual (SRMR) index was used, where values of 0.06 or less indicate an excellent
ﬁt and values of 0.08 or less indicate a good ﬁt [51]. In the same way, the coeﬃcients of the indices
of robust goodness-of-ﬁt of the proposed model, the compared ﬁt index (CFI) and the incremental
ﬁt index (IFI), were tested. For these indicators, a ﬁt with values above 0.90 is considered good [52].
To ﬁnalize, the root mean square error of approximation (RMSEA) is shown, with a score below
0.08 being considered a good ﬁt [53].
In the evaluation of the reliability of the scale, three measurements were taken into account:
the Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE) values for each
factor [54]. In addition, convergent validity was tested through the signiﬁcance of the factorial loads in
their respective dimensions and the values of the associated t-tests. Additionally, discriminant validity,
which has to do with seeing the clear distinction between any pair of constructs, was evaluated using
Int. J. Environ. Res. Public Health 2020, 17, 925
5 of 19
the method suggested by Fornell and Larcker [55]. This method conﬁrms discriminant validity if the
square root of the AVE value of a determined factor is greater than the correlation coeﬃcients between
the factor and any other in the proposed scale. The other criterion to assure discriminant validity is
that the correlations between the diﬀerent pairs of factors must be less than 0.85 [48].
After checking the psychometric properties of the running commitment scale, a cluster analysis
was performed to identify groups of participants with diﬀerent characteristics according to their
running commitment. This analysis was performed using the statistical program SPSS version 24.0 for
Windows (IBM, Armonk, NY, USA), with the 11 items of the running commitment scale. Two estimation
methods (hierarchical and nonhierarchical) of the cluster solution were combined to optimize the
results. The hierarchical cluster analysis was performed using the Ward clustering process and, as a
measure of similarity, the Euclidean distance squared. A nonhierarchical analysis was applied to
the groups proposed in the previous analysis through the K-means method using as initial centres
the means of the variables obtained for each cluster solution of the hierarchical analysis. To deﬁne
the characteristics of the group proﬁles and to evaluate predictive validity, ANOVAs and chi-square
tests were performed with variables that were not included in the initial analysis. The value of the
contingency coeﬃcient (C) was also used to check the intensity of the association and the size of the
eﬀect of the related variables.
3. Results
3.1. Sociodemographic Characteristics
Table 1 shows the sociodemographic characteristics. The respondents had an average age of 39.48
(SD = 9.21), 74.4% being men and 25.6% being women. In terms of occupation, most are employed
(88.4%). The majority was university studies (59.2%) and had an income level of less than 18,000 euros
per year (51.3%).
Table 1. Sociodemographic characteristics of the sample.
Variable
Response Option
Mean and Percentage
Age
39.48
(SD 1 = 9.21)
Gender
Male
74.4%
Female
25.6%
Occupation
Employee
88.4%
Unemployed
6.1%
Student
1.2%
Other (retired, pensioner, domestic tasks, etc.)
4.3%
Level of Studies
Primary
6.1%
Secondary
6.2%
Baccalaureate/ Professional training
28.5%
University
59.2%
Income Level
Less than 12,000 euros
27.0%
12,001–18,000 euros per year
24.3%
18,001–24,000 euros per year
18.2%
24,001–30,000 euros per year
14.2%
30,001–36,000 euros per year
6.9%
More than 36,001 euros per year
9.5%
1 SD = Standard Deviation.
3.2. Psychometric Properties of the Commitment to Running Scale
First, the properties of the items on the commitment-to-running scale were analysed by checking
the corrected correlation item–total values, as well as the mean, standard deviation, asymmetry,
and kurtosis values. Table 2 shows the statistics. The mean scores of ﬁve indicators were inverted as
indicated by Ruiz-Juan and Zarauz [25]. The values of the item–total corrected correlation coeﬃcients
Int. J. Environ. Res. Public Health 2020, 17, 925
6 of 19
were higher than the cutoﬀ point recommended by the literature (≥0.30) [56]. Additionally, the values
of asymmetry and kurtosis are acceptable for most variables because they are lower than 3.0 [57]
except for item CR7, of which the value exceeded the recommended cutoﬀ point for ensuring a normal
distribution of the data. On the other hand, indicators on the intentions of amateur runners showed
adequate reliability with a Cronbach alpha of 0.91, CR = 0.92, and AVE = 0.79.
Table 2. Mean, standard deviation, corrected item–total correlation, alpha if the item is removed,
asymmetry, and kurtosis values of the indicators of the running-commitment scale.
Number
Items
Means (SD) 1
R IT-c 2
α without
Item
Asymmetry
Kurtosis
CR1
I am looking forward to running
3.79 (0.93)
0.62
0.79
−0.58
0.21
CR2
Running is drudgery (R)
3.83 (1.12)
0.53
0.80
−0.68
−0.46
CR3
I do not enjoy running (R)
4.34 (0.91)
0.48
0.80
−1.54
2.21
CR4
Running is vitally important to me
3.23 (1.10)
0.59
0.79
−0.23
−0.51
CR5
Life is so much richer as a result of running
3.41 (1.07)
0.56
0.80
−0.47
−0.26
CR6
Running is pleasant
4.09 (0.75)
0.56
0.80
−0.80
1.39
CR7
I dread the thought of running (R)
4.72 (0.68)
0.30
0.82
−3.01
10.20
CR8
I would arrange or change my schedule to
meet my need to run
3.29 (1.19)
0.46
0.81
−0.37
−0.68
CR9
I have to force myself to run (R)
3.66 (1.12)
0.42
0.81
−0.46
−0.63
CR10
To miss a day’s run is a sheer relief (R)
4.41 (0.83)
0.39
0.81
−1.45
1.89
CR11
Running is the high point of my day
2.93 (1.15)
0.47
0.81
−0.09
−0.68
1 SD = Standard Deviation; 2 Corrected item–total correlation; (R) = Reverse scoring; 1 = Totally disagree,
2 = Disagree, 3 = Neither agree nor disagree, 4 = Agree, and 5 = Totally agree.
After checking the properties of the items, an EFA was carried out for the 11 items related to
the running commitment of the interviewed participants. The parallel analysis procedure suggested
grouping the indicators into two factors. It was not necessary to eliminate any indicators because all
of them had factor loads above 0.40 and no cross-factor loads exceeded this value in the two factors.
Table 3 shows the grouping of the indicators into the two factors: enthusiasm for running (6 items) and
aﬄiction from running (5 items).
Table 3. Rotated factor structure of the commitment-to-running scale of runners participating in
popular endurance races, communalities, eigenvalues, and explained variance.
Number
Items
F1
F2
Com. 1
Factor 1: Enthusiasm for running
CR1
I am looking forward to running
0.56
0.48
CR4
Running is vitally important to me
0.81
0.64
CR5
Life is so much richer as a result of running
0.78
0.59
CR6
Running is pleasant
0.40
0.37
CR8
I would arrange or change my schedule to meet my need to run
0.62
0.37
CR11
Running is the high point of my day
0.65
0.41
Factor 2: Aﬄiction from running
CR2
Running is drudgery
0.57
0.43
CR3
I do not enjoy running
0.67
0.48
CR7
I dread the thought of running
0.67
0.40
CR9
I have to force myself to run
0.56
0.34
CR10
To miss a day’s run is a sheer relief
0.67
0.43
G-H Index
0.85
0.80
Eigenvalue
4.01
2.01
Variance Explained (%)
36.51
18.28
Items
6
5
1 Com. = Communality.
Int. J. Environ. Res. Public Health 2020, 17, 925
7 of 19
To check the ﬁt of the model, the RMSR and gamma index or GFI coeﬃcients were analysed,
which showed values within the recommended cutoﬀ points: RMSR = 0.03 (<0.05) and GFI = 0.99
(>0.95). In addition, the generalized G-H index showed values higher than 0.80 in the two factors
detected by the EFA (0.85 for the running enthusiasm factor and 0.80 for the running aﬄiction factor),
indicating the possibility of good replicability of the dimensions in other studies [46]. The variance
explained by the 11 items grouped in the two factors was 54.78%.
On the other hand, a CFA was derived from the two-factor solution of the EFA and from the
unidimensional proposal of the scale presented by the authors of the original scale [21]. The CFA
considering the scale as unidimensional did not show a good ﬁt, as seen in the goodness-of-ﬁt indices
(χ2 = 2005.81 (df = 44); p < 0.01; SRMR = 0.125; RMSEA = 0.122 (conﬁdence interval (CI) = 0.116–0.128);
CFI = 0.73; IFI = 0.73)).
However, the factorial solution derived from the EFA by grouping the indicators into two factors
did show optimal model ﬁt values. The value of χ2 was signiﬁcant (χ2 = 653.95; df = 43; p < 0.05),
although the relationship between the value of χ2 and the degrees of freedom (normed chi-square)
was quite high since values below ﬁve are considered acceptable [51]. This is because the value of this
statistic is very sensitive to the size of the sample, which could erroneously indicate a poor adjustment
of the data [48,58]. For this reason, it is recommended that other goodness-of-ﬁt indices be used [48]:
SRMR, RMSEA, CFI, and IFI. These indicators showed a good ﬁt: (SRMR = 0.077; RMSEA = 0.076
(IC = 0.070–0.082); CFI = 0.90; IFI = 0.90)).
To analyse reliability, the Cronbach’s alpha, CR, and AVE measurements were observed (see Table 4).
The Cronbach’s alpha values were higher than the 0.70 recommended by the literature [54]. This criterion
was also fulﬁlled for the CR values [55], with values of 0.83 for the running enthusiasm factor and
0.77 for the running aﬄiction factor. Finally, for the AVE values, it was found that the two factors did
not have values higher than the 0.50 recommended by the literature [59]. According to Hatcher [60],
when the reliability of the construct is acceptable, a marginally low AVE value can be accepted
(see Table 4). Therefore, the decision was made to retain these two factors without combining them
into a single factor since the unidimensional solution did not oﬀer a good ﬁt.
Table 4. Factorial loads, Cronbach’s alpha, composite reliability, and average variance extracted values
from commitment to running scale indicators.
Number
Items
λ
α
CR 1
AVE 2
Factor 1: Enthusiasm for running
0.82
0.83
0.45
CR1
I am looking forward to running
0.68
CR4
Running is vitally important to me
0.79
CR5
Life is so much richer as a result of running
0.76
CR6
Running is pleasant
0.54
CR8
I would arrange or change my schedule to meet my need to run
0.61
CR11
Running is the high point of my day
0.62
Factor 2: Aﬄiction from running
0.76
0.77
0.40
CR2
Running is drudgery
0.66
CR3
I do not enjoy running
0.69
CR7
I dread the thought of running
0.57
CR9
I have to force myself to run
0.6
CR10
To miss a day’s run is a sheer relief
0.64
1 CR = Composite Reliability; 2 AVE = Average Variance Extracted.
To analyse convergent validity, it was found that the values of the t-tests associated with the
factorial loads of the items were higher than 1.96 (p < 0.05), ranging from 15.06 to 27.57. Regarding
discriminant validity, on the one hand, the correlation between the two factors was less than 0.85
(r = 0.35; p < 0.01). On the other hand, it was found that the square root of the AVE value was higher
than the correlations between pairs of factors. This criterion was also complied because the values of
the square root of the AVE were √AVE = 0.67 for factor 1 and √AVE = 0.63 for factor 2.
Int. J. Environ. Res. Public Health 2020, 17, 925
8 of 19
3.3. Identiﬁcation and Description of Clusters
A cluster analysis was conducted to identify groups with diﬀerent characteristics according to
their commitment to running among the interviewed runners participating in urban short-distance
races. Following the procedure recommended by Hair et al. [54], a hierarchical cluster analysis was ﬁrst
performed using Ward’s method of observing the increase in the agglomeration coeﬃcients between
clusters two and three, three and four, and four and ﬁve. After observing these coeﬃcients, the solutions
of two, three, four, and ﬁve groups were used to apply the second analysis of the k-median clusters
by using the initial centres from the hierarchical cluster analysis. It was considered appropriate to
contrast all the solutions because there are not many previous studies that identify clusters of amateur
runners according to their commitment to running, as mentioned in the theoretical framework.
This study used the solution of three clusters because it identiﬁed three groups of urban runners
with diﬀerent levels of commitment to running, helping to interpret and identify their sociodemographic
proﬁles and sports habits. It is important to note that the choice of an ideal cluster solution depends on
the theoretical foundations, common sense, and practical judgement of the researcher [54]. Table 5
shows the mean values (centroids) for each of the 11 variables of the commitment-to-running scale
introduced in the analysis and the results of the ANOVA test carried out to conﬁrm the signiﬁcance
of the diﬀerences between groups (F statistic and signiﬁcance level). In this table, it can be seen that
all the mean scores of the identiﬁed groups present statistically signiﬁcant diﬀerences (p < 0.001).
The variables that distinguish the clusters most correspond to the running enthusiasm factor: “Running
is vitally important to me” (F = 896.45) and “Life is so much richer as a result of running” (F = 768.53).
The item that diﬀerentiates the clusters least is “I dread the thought of running” (F = 47.61).
Table 5. Average scores for each variable in the three clusters (obtained through the k-averages method).
Number
Items
1 = Highly
Committed
(n = 650) (SD) 1
2 = Moderately
Committed
(n = 749) (SD) 1
3 = Lowly
Committed
(n = 407) (SD) 1
F
p-Value
Factor 1: Enthusiasm for running
4.15 (0.46)
3.41 (0.35)
2.43 (0.41)
2263.89
<0.001 *
CR1
I am looking forward to running
4.42 (0.64)
3.73 (0.73)
2.89 (0.87)
543.02
<0.001 *
CR4
Running is vitally important to me
4.08 (0.78)
3.16 (0.77)
2.00 (0.80)
896.45
<0.001 *
CR5
Life is so much richer as a result
of running
4.12 (0.78)
3.47 (0.74)
2.18 (0.87)
768.53
<0.001 *
CR6
Running is pleasant
4.51 (0.58)
4.01 (0.59)
3.58 (0.86)
263.28
<0.001 *
CR8
I would arrange or change my
schedule to meet my need to run
4.09 (0.91)
3.26 (0.93)
2.09 (0.97)
577.43
<0.001 *
CR11
Running is the high point of my day
3.71 (0.96)
2.83 (0.90)
1.87 (0.88)
516.87
<0.001 *
Factor 2: Aﬄiction from running
1.32 (0.32)
2.04 (0.65)
2.16 (0.68)
388.42
<0.001 *
CR2
Running is drudgery
1.46 (0.75)
2.39 (1.01)
2.91 (1.14)
320.47
<0.001 *
CR3
I do not enjoy running
1.16 (0.54)
1.89 (0.96)
2.04 (0.93)
192.12
<0.001 *
CR7
I dread the thought of running
1.06 (0.33)
1.46 (0.85)
1.31 (0.65)
66.28
<0.001 *
CR9
I have to force myself to run
1.73 (0.89)
2.65 (1.03)
2.77 (1.16)
190.17
<0.001 *
CR10
To miss a day’s run is a sheer relief
1.20 (0.52)
1.82 (0.89)
1.79 (1.90)
126.35
<0.001 *
1 SD = Standard Deviation; * Statistically signiﬁcant mean diﬀerences p < 0.001.
Cluster 1 was labelled “highly committed” (n = 650; 35.99%) because all indicators related to
Factor 1 about running enthusiasm present averages close to or above the value of four on the Likert
scale, which indicates a high degree of agreement with these statements. This group of runners presents
a high commitment to this physical activity because they consider running to be of vital importance
for their lives (M = 4.08), they would reorganize or change their schedule to satisfy the need to run
(M = 4.09), and they consider their lives to be much richer because they practice this physical activity
(M = 4.12). In addition, they show a high degree of agreement about the desire to run (M = 4.42) and
enjoy the experience of running (M = 4.51). However, they disagree with the aspects related to factor 2
about a sense of aﬄiction from running. For example, they tend to disagree with items related to a
sense of drudgery over the activity (M = 1.46), an absence of enjoyment when running (M = 1.16),
Int. J. Environ. Res. Public Health 2020, 17, 925
9 of 19
a feeling of dread over the practice of running continuously (M = 1.06), and a sense of relief when not
running for one day (M = 1.20).
Cluster 2 was identiﬁed as “moderately committed” (n = 749; 41.47%) and includes a higher
proportion of the interviewed runners; this group of runners is characterized by presenting average
scores with a tendency towards agreement on some indicators of the dimension of enthusiasm for
running but with a more moderate tendency than in cluster 1. Thus, the runners in this group consider
the practice of running to be a pleasant activity (M = 4.01) and are willing to practise it (M = 3.73).
They also show a positive tendency in considering that running enriches their lives (M = 3.47). However,
they do not consider the practice of this physical activity to be essential to their lives (M = 3.16) or
to be the high point of their day (M = 2.83). On the other hand, this group also disagrees with most
of the negative aspects (aﬄiction for running) associated with a commitment to running. However,
the indicator related to the need to force themselves to run shows a score close to the value of three on
the scale (M = 2.65), which would indicate some neutrality in the assessments of this group.
Cluster 3 was labelled “slightly committed” (n = 407; 22.54%) because they show a tendency
to disagree with many aspects related to enthusiasm for running. In this regard, they disagree with
the items stating that the practice of this physical activity is of vital importance for them (M = 2.00),
that their life is much richer because they practice running (M = 2.18), that the practice of this activity is
the high point of their day (M = 1.87), and that they reorganize their schedules to satisfy the need to run
(M = 2.09). This group only shows a tendency to agree that running is a pleasant activity (M = 3.58).
In the same vein as the other groups, these runners show disagreement with the aspects related to a sense
of affliction from running, although there is a tendency towards neutrality in the aspects referring to a sense
of drudgery over the practice of this activity (M = 2.91) and a need to force themselves to run (M = 2.77).
3.4. Proﬁle of the Groups
Table 6 describes the proﬁle of the runners that make up each group using other independent
variables (sociodemographic and sports habits variables) that allow us to ensure the predictive validity
of the groups, the percentages for each sociodemographic, and sports habits variable according to
the cluster.
Statistically signiﬁcant diﬀerences were observed among the groups in the case of the
sociodemographic variables related to age (F(2, 1803) = 5.73, p < 0.05) and level of studies (χ2(6) = 18.94,
p < 0.01), although the size of the eﬀects (contingency coeﬃcients) presented reduced values (see Table 6).
It was also found that there were statistically signiﬁcant diﬀerences in the mean scores among the
groups on the indicators related to running addiction: “Some days, even if I do not feel like running,
I do it anyway” (F(2, 1803) = 104.78, p ≤ 0.001), “I feel like I need to run at least once every day”
(F(2, 1803) = 150.05, p ≤ 0.001), and “I’ve stopped running for at least a week for some other reason
than injury” (F(2, 1803) = 46.84, p ≤ 0.001). Additionally, on the indicator related to future intentions
to recommend participation in popular running races to others, statistically signiﬁcant diﬀerences in
mean group scores were observed (F(2.1803) = 4.72, p < 0.01).
The highly committed group is characterized by an average age of 39.51 (SD = 9.33), with a
majority percentage of men (76.62%), which is higher than the other groups. It is veriﬁed that there is a
greater degree of agreement in this group with indicators related to running addiction, as they show a
tendency towards agreement on, for instance, the item stating that, some days, even if they do not feel
like running, they go running anyway (M = 3.71), with a signiﬁcantly higher average compared to
the other two groups. Signiﬁcantly higher diﬀerences, albeit with a more neutral trend in the average
scores, are also observed in this group for the indicator related to the need to run at least once every
day (M = 3.04) and lower diﬀerences for the indicator related to having stopped running for at least
one week for a reason that was not injury related (M = 3.04). Finally, the runners in this group strongly
agree with the items on their future intentions regarding participation, recommendation, and positive
comments about running popular races, with slightly higher scores on all these indicators than those
of the other two groups.
Int. J. Environ. Res. Public Health 2020, 17, 925
10 of 19
Table 6. Characteristics of the diﬀerent groups (clusters).
Variable
Response Option
1 = Highly Committed
(n = 650)
2 = Moderately Committed
(n = 749)
3 = Lowly Committed
(n = 407)
Age *
F(2, 1803) = 5.73, p = 0.03
39.51
(SD 1 = 9.33)
40.13
(SD 1 = 9.26)
38.22
(SD 1 = 8.79)
Gender
χ2(2) = 2.71, p = 0.26
Male
76.62%
73.03%
73.22%
Female
23.38%
26.97%
26.78%
Occupation
χ2(6) = 2.13, p = 0.91
Employee
88.15%
88.79%
87.96%
Unemployed
6.92%
5.47%
5.90%
Student
1.08%
1.34%
1.23%
Other (retired, pensioner, domestic
tasks, etc.)
3.85%
4.41%
4.91%
Level of studies **
χ2(6) =18.94, p = 0.004
C 2 = 0.10
Primary
8.46% (3)
5.47%
3.44%
Secondary
7.23%
6.14%
4.67%
Baccalaureate/Professional training
29.85%
27.64%
27.76%
University
54.46%
60.75%
64.13% (1)
Income level
χ2(10) = 13.71, p = 0.19
Less than 12,000 euros
29.38%
25.90%
25.06%
12,001–18,000 euros per year
25.85%
22.56%
24.82%
18,001–24,000 euros per year
15.54%
19.49%
20.15%
24,001–30,000 euros per year
14.00%
14.69%
13.51%
30,001–36,000 euros per year
7.23%
6.14%
7.86%
More than 36,001 euros per year
8.00%
11.21%
8.60%
How often you run during the week? ***
χ2(6) = 290.78, p ≤ 0.001
C 2 = 0.37
Five or more times a week
14.31% (2) (3)
4.01%
1.97%
Three to ﬁve times a week
62.46% (2) (3)
51.27% (3)
29.73%
Once or twice a week
21.69%
40.32% (1)
51.84% (1) (2)
Less frequently
1.54%
4.41% (1)
16.46% (1) (2)
Int. J. Environ. Res. Public Health 2020, 17, 925
11 of 19
Table 6. Cont.
Variable
Response Option
1 = Highly Committed
(n = 650)
2 = Moderately Committed
(n = 749)
3 = Lowly Committed
(n = 407)
Preferred distance in popular races ***
χ2(10) =119.49, p ≤ 0.001
C 2 = 0.25
Less than 7.5 km
25.23%
36.85% (1)
52.58% (1) (2)
Between 7.5 km and 10 km
28.15%
30.31%
27.27%
Between 10 km and 15 km
27.69% (3)
22.83% (3)
13.51%
Between 15 km and 20 km
10.15% (2) (3)
6.14%
4.91%
Between 20 km and 30 km
4.62% (3)
2.54%
1.72%
More than 30 km
4.15% (2)
1.34%
0.00%
How do you usually run?
χ2(2) = 0.34, p = 0.84
Alone
62.46%
61.15%
60.93%
Accompanied
37.54%
38.85%
39.07%
Level you consider you are as a runner ***
χ2(4) = 133.61, p ≤ 0.001
C 2 = 0.26
High level
8.31% (2) (3)
2.67%
1.47%
Intermediate
64.92% (2) (3)
53.14% (3)
39.07%
Low level
26.77%
44.19% (1)
59.46% (2) (3)
Are you a member of a sports club?
χ2(2) = 53.84, p ≤ 0.001
C 2 = 0.17
Yes
48.77% (2) (3)
40.05% (3)
26.04%
No
51.23%
59.95% (1)
73.96% (2) (3)
Are you sports federated?
χ2(2) = 10.47, p = 0.005
C 2 = 0.08
Yes
6.92% (2) (3)
3.60%
3.44%
No
93.08%
96.40% (1)
96.56% (1)
Distance usually run weekly (kilometres) ***
F(2, 1803) = 106.76, p ≤ 0.001
35.11
(SD 1 = 18.54)
26.16
(SD 1 = 14.65)
20.58
(SD 1 = 16.01)
Years running **
F(2, 1803) = 7.20, p = 0.01
8.04
(SD 1 = 8.95)
7.32
(SD 1 = 8.20)
6.08
(SD 1 = 6.68)
Participation in half marathons ***
F(2, 1803) = 20.70, p ≤ 0.001
7.99
(SD 1 = 22.17)
4.05
(SD 1 = 10.06)
2.30
(SD 1 = 6.33)
Int. J. Environ. Res. Public Health 2020, 17, 925
12 of 19
Table 6. Cont.
Variable
Response Option
1 = Highly Committed
(n = 650)
2 = Moderately Committed
(n = 749)
3 = Lowly Committed
(n = 407)
Participation in marathons ***
F(2, 1803) = 17.84, p ≤ 0.001
1.38
(SD 1 = 3.77)
0.74
(SD 1 = 2.74)
0.32
(SD 1 = 1.26)
Some days, even if I do not feel like running, I do it anyway ***
F(2, 1803) = 104.78, p ≤ 0.001
3.71
(SD 1 = 1.02)
3.35
(SD 1 = 0.99)
2.76
(SD 1 = 1.13)
I feel like I need to run at least once every day ***
F(2, 1803) = 150.05, p ≤ 0.001
3.04
(SD 1 = 1.20)
2.49
(SD 1 = 1.07)
1.84
(SD 1 = 0.97)
I have stopped running for at least a week for another reason that was not an injury ***
F(2, 1803) = 46.84, p ≤ 0.001
3.04
(SD 1 = 1.44)
3.58
(SD 1 = 1.17)
3.75
(SD 1 = 1.31)
Future intentions regarding participation in
urban popular races
I am willing to continue
participating in popular urban races
F(2, 1803) = 0.85, p = 0.45
4.42
(SD 1 = 1.03)
4.38
(SD 1 = 0.99)
4.35
(SD 1 = 0.91)
I will recommend participation in
popular urban races to others **
F(2, 1803) = 4.72, p = 0.009
4.48
(SD 1 = 3.77)
4.37
(SD 1 = 3.77)
4.32
(SD 1 = 3.77)
I will speak well of popular urban
races to others
F(2, 1803) = 2.04, p = 0.13
4.46
(SD 1 = 0.89)
4.39
(SD 1 = 0.86)
4.37
(SD 1 = 0.80)
1 SD = Standard Deviation; 2 C = Contingency Coeﬃcient; indications of statistically signiﬁcant relationship or statistically signiﬁcant mean diﬀerences: * p < 0.05; ** p ≤ 0 0.01; *** p ≤ 0.001;
(1) (2) (3) results are based on bilateral tests with a level of signiﬁcance 0.05. The results table shows for each signiﬁcant pair the key of the group of runners with the proportion of the
smallest column below the group of runners with the largest proportion of the column.
Int. J. Environ. Res. Public Health 2020, 17, 925
13 of 19
The moderately committed group is characterized by a higher average age of 40.13 (SD = 9.26),
signiﬁcantly higher than the slightly committed group, and a majority percentage of men (73.03%).
These runners tend towards agreement on the indicator related to having stopped running for at least
one week for a reason other than an injury (M = 3.58), with a signiﬁcantly higher average compared to
the higher commitment group level. In the same way as the other groups, they strongly agree with the
items on their future intentions regarding participation, recommendation, and positive comments on
popular races.
The third, slightly committed group is characterized by the lowest average age with 38.22
(SD = 8.79) and a majority percentage of men (73.22%). They tend towards agreement on the indicator
related to having stopped running for at least one week for a reason that was not injury related
(M = 3.75) with a signiﬁcantly higher average compared to the group with greater commitment to the
race. Following the trend observed in the other groups, they strongly agree on the item on their future
intentions regarding participation, recommendation, and positive comments on popular races.
4. Discussion
This study analyses the running commitment of runners participating in urban popular races
from the commitment-to-running scale validated with Spanish marathon runners. The analysis of
the psychometric properties of the scale showed that the grouping of the indicators into two factors
presented a better adjustment to the data collected in this study, identifying two dimensions related
to positive aspects (enthusiasm for running) and negative aspects (aﬄiction from running) of the
psychological construct of commitment to running. In previous works on the validation of the
running-commitment scale, the possibility of grouping the indicators under two factors was assessed
but was rejected due to the lack of interpretability and the high degree of uncertainty of the second
factor identiﬁed [25]. For this reason, it was decided to test the ﬁt of the scale on a single factor, although
the various goodness-of-ﬁt indices showed values distant from those recommended in the literature
as acceptable.
This study identiﬁed three groups of participants in urban popular races with diﬀerent
commitments to running: “highly committed”, “moderately committed”, and “slightly committed”.
The denomination of these groups was made from the interpretation of the average scores of the
variables on the commitment-to-running scale that allowed us to identify three groups with clearly
diﬀerent levels of commitment towards the practice of this physical activity.
The sociodemographic variables that contributed to signiﬁcantly diﬀerentiating the identiﬁed
groups were age and educational level. However, previous studies that have analysed the commitment
to running without segmenting respondents into groups did not ﬁnd signiﬁcant diﬀerences in the
items of the running-commitment scale according to age [15,25]. In other studies identifying groups
according to their motivations for running, age was found to be a variable that diﬀerentiated the
identiﬁed clusters [27,29,30]. The results varied according to gender, with some studies showing
diﬀerences in the commitment to running according to this variable [10,19,21,25,37], while other
studies, in the same vein as the results of this study, detected no signiﬁcant diﬀerences by gender [15].
It is important to note that the proﬁle of the runners in these studies was runners of long-distance races
such as half-marathons and marathons, diﬀerent from the sample analysed in this study. Regarding the
level of studies, in the work of Zarauz and Ruiz-Juan [19] on marathon runners, it was observed that a
lower level of studies signiﬁcantly predicted the commitment to running. In this study, it was found
that there were signiﬁcant disproportions of runners with a higher level of studies among runners with
a lower level of commitment to running. In any case, more studies on amateur short-distance runners
are needed to determine which sociodemographic variables contribute to diﬀerentiating subgroups of
runners according to their commitment to running.
On the other hand, variables related to sports habits (training frequency, preferred distance,
years of running, number of kilometres run weekly, level at which runners considered themselves to be,
participation in long-distance races, and membership of clubs and sports federations) also contributed
Int. J. Environ. Res. Public Health 2020, 17, 925
14 of 19
to signiﬁcantly diﬀerentiating the groups identiﬁed, conﬁrming the need to consider runners from a
heterogeneous point of view, as observed in other studies [27,29,30,61]. In previous studies, it was
found that weekly training frequency, number of years of running, and number of kilometres run were
positively related to commitment to running [10,21–23,25]. Similarly, a preference for participating in
long-distance running was associated with higher scores on commitment to running [25,37,62].
From the point of view of the variables related to the running-addiction scale, it is worth noting
that statistically signiﬁcant diﬀerences were found between the groups, proving the existence of a
higher level of involvement in the activity in runners with a high commitment to running. In the
theoretical framework, it has been emphasized that previous studies have proved that there is a high
correlation between higher values of commitment to running and addiction to this physical activity,
although both constructs may be inﬂuenced by diﬀerent variables. In a previous study, it was observed
that, in half-marathon runners, the variable that seemed to have most importance with respect to why a
half-marathon runner goes from being healthily committed to his sport practice to being pathologically
addicted to it is the number of kilometres he runs each week: if it is low, a healthy commitment
increases, while if it is high, in men, negative addiction increases [10]. In the present work, although
this ﬁnding was not empirically proven, it is observed that the runners in the highly committed group
train signiﬁcantly more kilometres per week than the other groups and, in addition, they show a
signiﬁcantly higher tendency to agree on statements related to running addiction, such as the item
stating that, some days, even if they do not feel like running, they practice the activity anyway. In this
sense, the points made by Ruiz-Juan, Zarauz, and Flores-Allende [10] could be conﬁrmed but in
runners who participated in urban popular races of reduced distance. In any case, more empirical
evidence is needed to conﬁrm the hypothesized relationship between the number of kilometres run
and running addiction in amateur short-distance runners.
On the other hand, analysis of the groups’ proﬁle veriﬁed that the group denominated as highly
committed presents a tendency to agree on most of the indicators related to the factor of enthusiasm to
run, indicating a clear commitment through the practice of this activity. In contrast, on the aspects
related to the aﬄiction factor from running, they show the inverse tendency, which also corroborates the
high commitment of these runners to this physical activity. This is the group with the highest proportion
of people with the lowest level of studies, in contrast to the group with the lowest commitment to
running, indicating a similar trend to that observed by Zarauz and Ruiz-Juan [19] on the inﬂuence of
this variable on the commitment to running. This is a group with sporting habits deﬁned by the constant
and regular practice of this physical activity: they tend to run alone, have a training frequency of three
or more times per week, display a preference for distances in popular races between 7.5 km and 15 km,
are members of sports clubs, have the highest average number of kilometres run weekly, have the
greatest number of years practising running, and have the highest average number of long-distance
races run. This type of runner has been identiﬁed by Leedy [37] as “committed runners”. Additionally,
Carmack and Martens [21] related the high commitment to running to running addiction; these runners
were later classed by Pargman [20] as addict-dependent. However, this concept should be considered
in a positive sense, that is, as deﬁned by this author, from the perspective that they are participants
who show pleasure, enjoyment, and joy in the practice of this sport, as identiﬁed through the average
scores observed on the indicators of the running enthusiasm factor.
With respect to the group known as moderately committed, a more neutral and moderate attitude
is observed in the indicators referring to the enthusiasm for running, highlighting aspects such as the
desire to run or pleasure in running while showing a neutral evaluation on items such as the statement
that the practice of this physical activity is the high point of the day or of vital importance for their lives.
In the case of items on the aﬄiction from running factor, they present a tendency towards disagreement,
albeit less pronounced than that of the group with greater commitment. Their sociodemographic proﬁle
diﬀers in that they are older than the rest of the groups and have a higher level of education than the
most committed group. The sports habits of this group present intermediate levels between the groups
with lesser commitment and greater commitment, with the highlights being that they tend to run alone,
Int. J. Environ. Res. Public Health 2020, 17, 925
15 of 19
their training frequency is three to ﬁve times per week, they display a preference for short-distance
races (less than 10 km), they run a shorter distance each weekly than the highly committed group,
and they have fewer years practising this physical activity. On the other hand, regarding the indicators
on the running addiction scale, they do not agree that they need to run at least once every day and
show a slight tendency towards agreement on the item regarding having stopped running for reasons
other than an injury. This runner proﬁle is characterized by a recreational type of orientation and less
commitment to the practice of running despite considering it an enjoyable and positive experience
for their lives. Previous work on motivation and commitment to running has identiﬁed proﬁles of
recreational runners [37], relational runners [5], socializing hedonists [27], and social competitors [29],
who may have similarities in terms of their sporting habits and sociodemographic proﬁle. This group
could be identiﬁed with a healthier proﬁle from a psychological point of view, since they do not
show symptoms of possible running addiction, as could be deduced from the scores of the indicators
extracted from the running addiction scale.
Finally, the group dubbed slightly committed is the group with the lowest level of commitment
to running because it is the group that disagrees most with the indicators related to enthusiasm for
running (running is of vital importance to their life; the practice of enriches their life; they reorganize
schedules in their daily life; and running is the highlight of the day). Unlike for the other two groups,
the only indicator with a positive trend for this group is the fact that they consider running a pleasant
activity. In the same way, the other groups disagree with the aspects related to aﬄiction from running.
However, the slightly committed group has a certain tendency towards low motivation and considers
the activity to be drudgery, as the tendency towards neutrality observed in some scores of the running
aﬄiction factor points out. From the point of view of sociodemographic characteristics, this is the
youngest group and has the highest level of studies. They are runners who train less frequently (once or
twice a week), prefer short-distance races (less than 7.5 km), are less likely to have aﬃliations with
sports clubs, run fewer kilometres weekly, and have fewer years practising this sport. There is a lower
degree of dependence on the activity, as seen from the scores on the running-addiction indicators,
giving a secondary role to the practice of running.
4.1. Practical Implications
The objective of this research was to analyze the commitment of runners among urban runners
and to segment and classify the runners. Research in this area and on commitment to running is
limited, especially among urban short-distance runners. The groups of runners were identiﬁed and
described sociodemographic proﬁle and sports habits: highly committed, moderately committed,
and slightly committed. The conclusions of the work can be useful for the administration, management,
and organization of local sports in terms of knowing the proﬁles of participants in popular races
and of developing measures to improve the quality of life of the participants. In this regard, a high
commitment to running can imply risks for health and can devolve into addictive behaviours that
must be prevented to ensure that a runner is healthily committed to their sport practice rather than
pathologically addicted to it. Running is an activity that has experienced an important boom in
Spanish cities such as Valencia, requiring the implementation of sports policies and strategic plans that
respond to the social demands of this collective. For this reason, it is advisable to oﬀer information
and public services (sports, health, psychology, and education) to runners through the organizations
and communication initiatives associated with this popular activity. Additionally, the identiﬁcation
of diﬀerent proﬁles, characteristics, and habits can beneﬁt sport entities and public organizations to
encourage sport activity.
4.2. Limitations and Future Lines of Research
This work presents some limitations that should be taken into account when generalizing and
extrapolating the results to other populations; for example, the type of sampling used does not allow
for generalization of the results to the set of urban amateur runners. In carrying out f more studies
Int. J. Environ. Res. Public Health 2020, 17, 925
16 of 19
of diﬀerent populations of amateur runners, eﬀorts should be made to identify subgroups of the
population of urban runners in diﬀerent countries and regions with the aim of elaborating typologies of
runners at the international level as well as identifying possible diﬀerences according to sociocultural
and socioeconomic characteristics. Similarly, the relationship between commitment to running and
possible addictions to this physical activity in runners who are starting out in the sport or who
participate in shorter-distance races should be explored in greater depth with the aim of preventing
future behaviour that could be dangerous to health.
5. Conclusions
The aim of this paper is to analyse the commitment to running among urban runners by identifying
groups with a greater or lesser commitment to this sport. Three groups of urban amateur runners with
diﬀerent levels of commitment to running were identiﬁed: highly committed, moderately committed,
and slightly committed. Also, the study identiﬁes two factors within the psychological construct of
commitment to running: enthusiasm for running and aﬄiction from running.
Sociodemographic variables related to age and educational level contribute to diﬀerentiating the
groups of amateur runners according to their commitment to running. It is also found that most of the
variables related to sports habits contribute to diﬀerentiating the groups: the frequency with which
they go out to run, their preferred distance in popular races, the number of years they have practised
running, the number of kilometres run weekly, the level they consider themselves to be at as runners,
participation in long-distance races, and membership in clubs and sports federations.
Highly committed runners positively value the aspects related to enthusiasm for running and
show the reverse trend in the aspects related to the aﬄiction from running.
Moderately committed runners are characterized by a moderate attitude towards running
enthusiasm and a tendency to disagree on aspects related to running aﬄiction.
Slightly committed runners show a low commitment to the practice of running, with a tendency
towards disagreement on the indicators related to enthusiasm for running except the one related to
considering it a pleasant activity. They disagree with the aspects related to the aﬄiction from running,
although with a tendency to neutrality when considering running to be a drudgery and an activity for
which they have low motivation.
Author Contributions: Conceptualization, D.P.-C. and M.H.G.-S.; methodology, D.P.-C. and M.A.D.S.; formal
analysis, M.H.G.-S. and M.A.D.S.; writing—original draft preparation, D.P.-C., M.H.G.-S. and M.A.D.S.;
writing—review and editing, D.P.-C. and M.A.D.S. All authors have read and agree to the published version of
the manuscript.
Funding: This research received no external funding.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
References
1.
Scheerder, J.; Breedveld, K.; Borgers, J. Who Is Doing a Run with the Running Boom? In Running across
Europe: The Rise and Size of One of the Largest Sport Markets; Scheerder, J., Breedveld, K., Borgers, J., Eds.;
Palgrave Macmillan: London, UK, 2015; pp. 1–27.
2.
Llopis-Goig, L.; Vilanova, A. A Sociological Analysis of the Evolution and Characteristics of Running.
In Running across Europe: The Rise and Size of One of the Largest Sport Markets; Scheerder, J., Breedveld, K.,
Borgers, J., Eds.; Palgrave Macmillan: London, UK, 2015; pp. 225–239.
3.
Wicker, P.; Breuer, C. Understanding the Importance of Organizational Resources to Explain Organizational
Problems: Evidence from Nonproﬁt Sport Clubs in Germany. Volunt. Int. J. Volunt. Nonproﬁt Organ. 2013,
24, 461–484. [CrossRef]
4.
Guaita, J.M.; Roig-Tierno, N.; Mas-Tur, A. Impact of non-professional runner clubs: An AHP approach to the
economic, environmental and sociocultural factors. J. Sports Econ. Manag. 2018, 8, 63–72.
5.
Llopis Goig, R.; Llopis Goig, D. Una tipología sociocultural de los corredores populares en España. Apunt. Educ.
Física Deportes 2012, 108, 9–16. [CrossRef]
Int. J. Environ. Res. Public Health 2020, 17, 925
17 of 19
6.
Scanlan, T.K.; Carpenter, P.J.; Simons, J.P.; Schmidt, G.W.; Keeler, B. An Introduction to the Sport Commitment
Model. J. Sport Exerc. Psychol. 1993, 15, 1–15. [CrossRef]
7.
Scanlan, T.K.; Carpenter, P.J.; Simons, J.P.; Schmidt, G.W.; Keeler, B. The Sport Commitment Model:
Measurement Development for the Youth-Sport Domain. J. Sport Exerc. Psychol. 1993, 15, 16–38. [CrossRef]
8.
Scanlan, T.K.; Russell, D.G.; Beals, K.P.; Scanlan, L.A. Project on Elite Athlete Commitment (PEAK): II. A Direct
Test and Expansion of the Sport Commitment Model with Elite Amateur Sportsmen. J. Sport Exerc. Psychol.
2003, 25, 377–401. [CrossRef]
9.
Glasser, W. Positive Addiction; Harper & Row: Oxford, UK, 1976.
10.
Ruiz-Juan, F.; Zarauz, A.; Flores-Allende, G. Dependencia al entrenamiento y competición en corredores de
fondo en ruta. Cult. Cienc. Deporte 2016, 11, 149–155.
11.
Meeusen, R.; Duclos, M.; Gleeson, M.; Rietjens, G.; Steinacker, J.; Urhausen, A. Prevention, diagnosis and
treatment of the Overtraining Syndrome. Eur. J. Sport Sci. 2006, 6, 1–14. [CrossRef]
12.
Lu, F.J.-H.; Eva, Y.-W.; Wang, J.-M.; Chang, J.-N.; Huang, M.-Y.; Wang, C.-H. Exercisers’ Identities and
Exercise Dependence: The Mediating Eﬀect of Exercise Commitment. Percept. Mot. Ski. 2012, 115, 618–631.
[CrossRef]
13.
Zarauz-Sancho, A.; Ruiz-Juan, F.; Flores-Allende, G.; Giráldez, V.A. Variables predictoras de la percepción
del éxito: Aspectos diferenciales en corredores de ruta/Predictor Variables of the Perception of Success:
Diﬀerential Aspects in Route Runners. Rev. Int. Med. Cienc. Act. Física Deporte 2016, 16, 601–615. [CrossRef]
14.
Karr, T.M.; Zunker, C.; Thompson, R.A.; Sherman, R.T.; Erickson, A.; Cao, L.; Crosby, R.D.; Mitchell, J.E.
Moderators of the association between exercise identity and obligatory exercise among participants of an
athletic event. Body Image 2013, 10, 70–77. [CrossRef] [PubMed]
15.
Chapman, C.L.; De Castro, J.M. Running addiction: Measurement and associated psychological characteristics.
J. Sports Med. Phys. Fit. 1990, 30, 283–290.
16.
Horton, R.S.; Mack, D.E. Athletic identity in marathon runners: Functional focus or dysfunctional
commitment? J. Sport Behav. Mob. 2000, 23, 101–119.
17.
Masters, K.S.; Ogles, B.M.; Jolton, J.A. The Development of an Instrument to Measure Motivation for
Marathon Running: The Motivations of Marathoners Scales (MOMS). Res. Q. Exerc. Sport 1993, 64, 134–143.
[CrossRef] [PubMed]
18.
Zarauz, A.; Ruiz-Juan, F. Super-adherencia del maratoniano: Variables predictoras y diferencias de género.
Univ. Psychol. 2012, 11, 895–907.
19.
Zarauz, A.; Ruiz-Juan, D.F. Compromiso y adicción negtiva al entrenamiento y competición de los
maratonianos. Rev. Int. Med. Cienc. Act. Física Deporte 2011, 11, 817–834.
20.
Pargman, D. The way of the runner: An examination of motives for running. In Psychology in Sports: Methods
and Applications; Suinn, R.M., Ed.; Burguess: Minneapolis, MN, USA, 1980; pp. 90–98.
21.
Carmack, M.A.; Martens, R. Measuring Commitment to Running: A Survey of Runners’ Attitudes and
Mental States. J. Sport Exerc. Psychol. 1979, 1, 25–42. [CrossRef]
22.
Joseph, P.; Robbins, J.M. Worker of runner? The impact of commitment to running and work on self
identiﬁcation. In Psychology of Running; Sacks, M.H., Sacks, M.L., Eds.; Human Kinnetics: Champaign,
IL, USA, 1981; pp. 131–146.
23.
Thornton, E.W.; Scott, S.E. Motivation in the committed runner: Correlations between self-report scales and
behaviour. Health Promot. Int. 1995, 10, 177–184. [CrossRef]
24.
Summers, J.J.; Machin, V.J.; Sargent, G.I. Psychosocial Factors Related to Marathon Running. J. Sport Exerc.
Psychol. 1983, 5, 314–331. [CrossRef]
25.
Ruiz-Juan, F.; Zarauz, A. Validation of the Spanish version of the Commitment to Running Scale (CR).
Estud. Psicol. 2011, 32, 195–207. [CrossRef]
26.
Llopis Goig, D.; Llopis Goig, R. Razones para participar en carreras de resistencia. Un estudio con corredores
aﬁcionados. Cult. Cienc. Deporte 2006, 2, 33–40.
27.
Parra-Camacho, D.; González-Serrano, M.H.; González-García, R.J.; Calabuig, F. Sporting Habits of Urban
Runners: Classiﬁcation According to Their Motivation. Int. J. Environ. Res. Public Health 2019, 16, 4990.
[CrossRef]
28.
Ruiz-Juan, F.; Zarauz, A. Variables que hacen adicto negativamente a correr al maratoniano español.
Retos Nuevas Tend. Educ. Física Deporte Recreación 2015, 21, 38–42.
Int. J. Environ. Res. Public Health 2020, 17, 925
18 of 19
29.
Rohm, A.J. A Mixed-Method Approach for Developing Market Segmentation Typologies in the Sports
Industry. Sport Mark. Q. 2006, 15, 29–39.
30.
Ogles, B.M.; Masters, K.S. A typology of marathon runners based on cluster analysis of motivations. J. Sport
Behav. 2003, 26, 69–85.
31.
Ruiz-Juan, F.; Zarauz, A. Análisis de la motivación en corredores de maratón españoles. Rev. Latinoam. Psicol.
2014, 46, 1–11. [CrossRef]
32.
Clough, P.; Shepherd, J.; Maughan, R. Motives for Participation in Recreational Running. J. Leis. Res. 1989,
21, 297–309. [CrossRef]
33.
Ogles, B.M.; Masters, K.S.; Richardson, S.A. Obligatory running and gender: An analysis of participative
motives and training habits. Int. J. Sport Psychol. 1995, 26, 233–248.
34.
Besomi, M.; Leppe, J.; Martínez, M.J.; Enríquez, M.I.; Mauri-Stecca, M.V.; Sizer, P.S. Running motivations
within diﬀerent populations of Chilean urban runners. Eur. J. Physiother. 2017, 19, 8–11. [CrossRef]
35.
Nikolaidis, P.T.; Chalabaev, A.; Rosemann, T.; Knechtle, B. Motivation in the Athens Classic Marathon:
The Role of Sex, Age, and Performance Level in Greek Recreational Marathon Runners. Int. J. Environ. Res.
Public. Health 2019, 16, 2549. [CrossRef]
36.
Zarauz, A.; Ruiz-Juan, F. Psychometric properties of the Spanish version of the Running Addiction Scale
(RAS). Span. J. Psychol. 2011, 14, 967–976.
37.
Leedy, M.G. Commitment to distance running: Coping mechanism or addiction? J. Sport Behav. Mob. 2000,
23, 255–270.
38.
Andersen, J.J. The State of Running 2019 | RunRepeat. Available online: https://runrepeat.com/state-of-
running (accessed on 24 January 2020).
39.
Consejo Superior de Deportes. Encuesta de Hábitos Deportivos en España 2015; Ministerio de Educación,
Cultura y Deporte: Madrid, Spain, 2015.
40.
Zeithaml, V.A.; Berry, L.L.; Parasuraman, A. The Behavioral Consequences of Service Quality. J. Mark. 1996,
60, 31–46. [CrossRef]
41.
Lloret-Segura, S.; Ferreres-Traver, A.; Hernández-Baeza, A.; Tomás-Marco, I. El análisis factorial exploratorio
de los ítems: Una guía práctica, revisada y actualizada. An. Psicol. Ann. Psychol. 2014, 30, 1151–1169.
[CrossRef]
42.
Timmerman, M.E.; Lorenzo-Seva, U. Dimensionality assessment of ordered polytomous items with parallel
analysis. Psychol. Methods 2011, 16, 209–220. [CrossRef]
43.
Tanaka, J.S.; Huba, G.J. A general coeﬃcient of determination for covariance structure models under arbitrary
GLS estimation. Br. J. Math. Stat. Psychol. 1989, 42, 233–239. [CrossRef]
44.
Harman, H.H. Análisis Factorial Moderno; Saltés: Madrid, Spain, 1980.
45.
Ruiz, M.Á.; Pardo, A.; San Martín, R. Modelos de ecuaciones estructurales. Papeles Psicólogo 2010, 31, 34–45.
46.
Ferrando, P.J.; Lorenzo-Seva, U. Assessing the Quality and Appropriateness of Factor Solutions and Factor
Score Estimates in Exploratory Item Factor Analysis. Educ. Psychol. Meas. 2018, 78, 762–780. [CrossRef]
47.
Chou, C.-P.; Bentler, P.M.; Satorra, A. Scaled test statistics and robust standard errors for non-normal data in
covariance structure analysis: A Monte Carlo study. Br. J. Math. Stat. Psychol. 1991, 44, 347–357. [CrossRef]
48.
Kline, R.B. Principles and Practice of Structural Equation Modeling, 4th ed.; Guilford Publications: New York,
NY, USA, 2015.
49.
Satorra, A.; Bentler, P.M. Corrections to test statistics and standard errors in covariance structure analysis.
In Latent Variables Analysis:
Applications for Developmental Research; von Eye, A., Clogg, C.C., Eds.;
Sage Publications, Inc.: Thousand Oaks, CA, USA, 1994; pp. 399–419.
50.
Wheaton, B.; Muthén, B.; Alwin, D.F.; Summers, G.F. Assessing Reliability and Stability in Panel Models.
Sociol. Methodol. 1977, 8, 84–136. [CrossRef]
51.
Hu, L.; Bentler, P.M. Cutoﬀ criteria for ﬁt indexes in covariance structure analysis: Conventional criteria
versus new alternatives. Struct. Equ. Model. Multidiscip. J. 1999, 6, 1–55. [CrossRef]
52.
MacCallum, R.C.; Austin, J.T. Applications of Structural Equation Modeling in Psychological Research.
Annu. Rev. Psychol. 2000, 51, 201–226. [CrossRef] [PubMed]
53.
Browne, M.W.; Cudeck, R. Alternative ways of assessing model ﬁt. In Testing Structural Equation Models;
Bollen, K.A., Long, J.S., Eds.; Sage: Newbury Park, CA, USA, 1993; pp. 136–162.
54.
Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis, 7th ed.; Pearson: London,
UK, 2014.
Int. J. Environ. Res. Public Health 2020, 17, 925
19 of 19
55.
Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and
Measurement Error. J. Mark. Res. 1981, 18, 39–50. [CrossRef]
56.
Nunnally, J.C. Teoría Psicométrica; McGraw-Hill: Madrid, Spain, 1995.
57.
Chou, C.-P.; Bentler, P.M. Estimates and tests in structural equation modeling. In Structural Equation Modeling:
Concepts, Issues, and Applications; Sage Publications, Inc.: Thousand Oaks, CA, USA, 1995; pp. 37–55.
58.
Schumacker, R.E.; Lomax, R.G.; Lomax, R.G. A Beginner’s Guide to Structural Equation Modeling; Routledge:
Mahwah, NJ, USA, 2012.
59.
Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94.
[CrossRef]
60.
Hatcher, L. A Step-by-Step Approach to Using the SAS System for Factor Analysis and Structural Equation Modelling;
SAS Institute: Cary, NC, USA, 1994.
61.
Vos, S.; Scheerder, J.; Boen, F.; Feys, J. A Typology of Runners: Implications for Marketing Strategies.
In Proceedings of the European Sport Management Conference, Heidelberg, Germany, 10–13 September 2008;
pp. 321–323.
62.
Funk, D.; Jordan, J.; Ridinger, L.; Kaplanidou, K. Capacity of Mass Participant Sport Events for the
Development of Activity Commitment and Future Exercise Intention. Leis. Sci. 2011, 33, 250–268. [CrossRef]
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Amateur Runners' Commitment: An Analysis of Sociodemographic and Sports Habit Profiles.
Parra-Camacho, David,Alonso Dos Santos, Manuel,González-Serrano, María Huertas