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Motivation dimensions for running a marathon: A new model emerging from the Motivation of Marathon Scale (MOMS)

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  • The Academic College at Wingate

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Purpose: The aim of this study was to test and expand the Motivation of Marathoners Scale (MOMS) model (Masters et al., 1993). Methods: The MOMS questionnaire was distributed to 306 male and female marathon runners (age range: 20–77 years) with experience in marathon running (range: 1–44 runs). A confirmatory factor analysis (CFA) revealed that the original model failed to fit the data. Hence, exploratory factor analysis (EFA) was performed to test the best factorial solution for the current data, and a subsequent CFA was performed on the revised factorial structure. Then, a series of EFAs using maximum likelihood factor extraction method were performed. Results: The best structure solution for model-data fit resulted in 11 factors: psychological coping—emotional-related coping, psychological coping—everyday-life management, life meaning, self-esteem, recognition, affiliation, weight concerns, general health orientation—reduced disease prevalence and longevity, general health orientation—keep fit, competition, and personal goal achievement. Conclusion: This study provides a sound and solid framework for studying motivation for physically demanding tasks such as marathon runs, and needs to be similarly applied and tested in studies incorporating physical tasks which vary in mental demands.
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Original article
Motivation dimensions for running a marathon: A new model emerging
from the Motivation of Marathon Scale (MOMS)
Sima Zach
a,
*, Yan Xia
b
, Aviva Zeev
a
, Michal Arnon
a
, Noa Choresh
a
, Gershon Tenenbaum
b
a
Zinman College for Physical Education and Sport Sciences, Wingate Institute, Netanya 42902, Israel
b
Sport and Exercise Psychology Department of Educational Psychology and Learning Systems, College of Education, Florida State University,
Tallahassee, FL 32306-4453, USA
Received 21 May 2015; revised 5 July 2015; accepted 10 July 2015
Available online
Abstract
Purpose: The aim of this study was to test and expand the Motivation of Marathoners Scale (MOMS) model (Masters et al., 1993).
Methods: The MOMS questionnaire was distributed to 306 male and female marathon runners (age range: 20–77 years), with experience in
marathon running (range: 1–44 runs). A confirmatory factor analysis (CFA) revealed that the original model failed to fit the data. Hence,
exploratory factor analysis (EFA) was performed to test the best factorial solution for the current data, and a subsequent CFA was performed on
the revised factorial structure. Then, a series of EFAs using maximum likelihood factor extraction method were performed.
Results: The best structure solution for model-data fit resulted in 11 factors: psychological coping—emotional-related coping, psychological
coping—everyday-life management, life meaning, self-esteem, recognition, affiliation, weight concerns, general health orientation—reduced
disease prevalence and longevity, general health orientation—keep fit, competition, personal goal achievement.
Conclusion: This study provides a sound and solid framework for studying motivation for physically demanding tasks such as marathon runs, and
needs to be similarly applied and tested in studies incorporating physical tasks which vary in mental demands.
© 2015 Production and hosting by Elsevier B.V. on behalf of Shanghai University of Sport.
Keywords: Exercise adherence; Marathon; Motives; Psychological characteristics
1. Introduction
The number of recreational runners who complete a mara-
thon, a running race of 42.2 km, has significantly increased in
the last 30 years.
1
Data from the USA show a rise from 22,000
runners in 1977 to more than 407,000 runners in 2007.
2,3
In Israel the marathon has also become increasingly
popular, from 938 runners in 2008 to 6320 in 2014
(
http://www.raceview.net/). This change is attributed mainly to
the fact that marathon races are no longer limited to the Olym-
pics or reserved for the elite athletes who train for important
competition.
4
In recent years, runners come from different
demographic and socio-economic strata who run for both rec-
reational and competitive reasons.
5
A marathon runner must adopt training habits and a lifestyle
behavior which is far beyond what is defined as recreational
exercise, and beyond what is recommended for acquiring the
basic health benefits of exercise.
6
Such behavior requires
demanding psychological, physiological, and financial
resources, with the high costs, and not necessarily positive.
7
Motives for running the marathon have been widely
explored.
8–13
Masters et al.
12
developed the Motivation for
Marathoners Scale (MOMS) and identified four main catego-
ries of motives: (1) psychological motives included maintaining
or enhancing self-esteem (e.g., “to improve my sense of self-
worth”), providing a sense of life meaning (e.g., “to make my
life more complete”), and problem solving or coping with nega-
tive emotions (e.g., “to become less anxious”); (2) social
motives included desire to affiliate with other runners (e.g., “to
socialize with other runners”) and to receive recognition or
approval from others (e.g., “to earn respect of peers”); (3)
physical motives for running included general health (e.g., “to
become more physically fit”) and benefits and weight concern
(e.g., “to look leaner”); and (4) achievement motives included
competition with other runners (e.g., “to see how high I can
place”) and personal goal achievement (e.g., “to push myself
beyond my current limits”).
Peer review under responsibility of Shanghai University of Sport.
* Corresponding author.
E-mail address:
simaz@wincol.ac.il (S. Zach).
http://dx.doi.org/10.1016/j.jshs.2015.10.003
2095-2546/© 2015 Production and hosting by Elsevier B.V. on behalf of Shanghai University of Sport.
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Using the MOMS, research was conducted to gain insight
regarding motivation for running the marathon among groups
with different demographic backgrounds. For example, Masters
and Ogles
11
documented the motivation characteristics of mara-
thon runners who varied in their participation experience. The
most experienced veterans, who had participated in more than
three marathons, were motivated more by social and competi-
tive reinforcements than by personal accomplishment or inter-
nal psychological rejuvenation. The mid-level experienced
runners, after their second or third marathon, were primarily
motivated by personal performance enhancement and psycho-
logical rewards. For the rookie marathon runners, self-esteem
appeared to be a more important motivation than for the more
experienced runners. In addition, since rookies had not yet
realized marathon goal accomplishment, they were less con-
cerned with performance improvement.
Havenar and Lochbaum
9
examined dropouts compared to
race finishers, and found that dropouts rated social motives and
weight concerns as significantly more important than did the
finishers. Others, such as Ziegler,
10
studied gender differences.
He examined the perceived benefits of marathon running in
males and females, and reported that men perceived running to
be more beneficial than did women, while women felt that
running had a positive effect on self-image and that their lives
were richer because of running—more so than men. Deaner
et al.
8
compared marathon performance as a predictor of com-
petitiveness and training between men and women. Their results
showed that the males reported significantly greater competi-
tiveness than the females.
Furthermore, Ogles and Masters
13
found that young mara-
thon participants (20–28 years) reported being more motivated
by personal goal achievement than did the older marathon
runners (age 50), and that older runners were more motivated
by general health orientation, weight concerns, life meaning,
and affiliation with other runners. In order to further understand
runners’ motives, Ogles and Masters
13
conducted a cluster
analysis based on a motivational profile and demographic and
training characteristics of 1519 marathon runners. Their analy-
sis yielded five definable subgroups: running enthusiasts,
lifestyle managers, personal goal achievers, personal accom-
plishers, and competitive achievers. Personal motives were
endorsed most often across all groups.
Nevertheless, these studies concentrated solely on motive
identification, and did not consider any conceptual framework
for either developing the MOMS or supporting their findings.
The bottom-up procedure was used to develop the MOMS.
Furthermore, a substantial change in the demographics of
marathon runners in recent years
14
necessitates a new look at
the motives for engaging in such a physically and mentally
demanding endeavor.
In this study we test the validity of the MOMS in a new sample
of marathon runners using the Self-Determination Theory (SDT)
15
as a conceptual framework. According to the SDT, motivational
states exist along a self-determination continuum that ranges from
no intention to act (i.e., amotivation—the least self-determined
form of motivation) at one end to intrinsic motivation at the other
end (representing the most self-determined form of motivation).
Between these two ends, extrinsically motivated behaviors are
located, varying in the extent to which their regulation is self-
determined from the least self-determined form of extrinsic
motivation—external regulation, to the most self-determined form
of extrinsic motivation—integrated regulation.
Ryan and Deci
16
argued that an individual is situated on the
motivational continuum according to the degree to which com-
petence, autonomy, and relatedness, three psychological needs,
are satisfied. Numerous studies have demonstrated that envi-
ronments encouraging competence, autonomy, and relatedness
produce persistence and other motivational consequences.
17,18
The question of whether such consequences also exist in mara-
thon runners is very intriguing.
Considering SDT postulations, we sought to better under-
stand marathon runners’ motives. Furthermore, we maintain that
recognizing the reasons for people’s motives may be valuable for
several reasons. First, such knowledge may be beneficial for
understanding the reasons individuals drop out of exercise pro-
grams, as well as their barriers to engaging in exercise.
19
Second,
since the psychological influence on exercise behavior may be
modifiable, determining the psychological characteristics of the
marathon runner is pertinent. Third, understanding the adherent
behavior required for persistence in training for participation in
the marathon may assist in developing effective interventions for
enhancing exercise motivation and adherence in order to assimi-
late a life-long active lifestyle.
10,20–22
Such knowledge may assist
the runner in applying the determination to finish a marathon to
other demanding challenges in life.
21
Consequently, we sought a better understanding of the
motives to participate in a physically and mentally demanding
task. Masters et al.’s
12
study was published more than 20 years
ago. During this period sporting events generate buzz that
attracts various response among participants, their families,
sport event’s organizers and marketers, and attracts many par-
ticipants to the events.
4
Social media platforms that did not exist
in the past are strongly present nowadays.
23
However, implica-
tions of these processes on motivation for sport participation
were not researched so far. Following findings showing that
recreational marathon runners nowadays are mainly intrinsi-
cally or task-related motivated,
24,25
we assumed that motives to
marathon participation have changed along these years. More-
over, the fact that the participants in our study are Israelis, and
that the questionnaire was translated from English to Hebrew,
brings about the cross-cultural issue. Along the line of
others,
5,26,27
reporting that cultural aspects are related to running
motives our study aimed at examining evidence for a cross-
cultural validation of the MOMS model,
12
to explore the best
factorial structure solution associated with the current data. We
hypothesized that the social (meaning of life-self esteem) and
health—mental and physical—domains would result as main
motives of the current cohort of participants.
2. Methods
2.1. Participants
Permission of IRB of Zinman College was obtained prior to
the beginning of the study. Three-hundred and six participants
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2 S. Zach et al.
(233 men, 58 women, 15 genders not designated), age ranging
20–77 years (41.87 ± 8.58, mean ± SD), filled out the Hebrew
version of the MOMS questionnaire.
12
The inclusion criterion
required that the participant had already completed at least one
marathon. Distribution of participants according to their age
showed that almost half (47%) of the participants were in their
forties. Distribution according to experience in marathon
running showed that 77 had participated in one marathon, 131
in 2–4 marathons, and 98 in five or more marathons, among
them 11 who had participated in more than 20 marathons (range
1–44; 4.27 ± 5.56). Distribution of their training session habits
during a regular season, when not preparing for a coming event,
showed that 76% practiced 4–6 times a week, 8% practiced
more than 6 times a week and 15.9% practiced less than 4 times
a week (range 2–11; 4.76 ± 1.48). The range of training session
duration lasted between 30 and 255 min (78.01 ± 27.12); 78.1%
of the participants ran between 60 and 90 min per session, 9%
ran less, and the rest ran longer than 90 min.
2.2. Questionnaire
Demographic questions were asked, including gender and
age, and training habits questions such as the number of weekly
training sessions and training session duration, in addition to
the number of completed marathons.
The MOMS was translated from English to Hebrew using
back translation and the committee approach.
28,29
A group of
three bilingual translators translated the questionnaire from the
language of origin (English) to the target language (Hebrew).
Then a translator translated the questionnaire back into the
original language. In a committee form, the four experts
reached consensus on language discrepancies and produced the
final version. The scale is comprised of 56 questions rated on a
7-point Likert-type scale (1 = not a reason, 7 = a most impor-
tant reason). The original questionnaire had psychometric
properties as follows: (a) reliability—internal consistency—
α coefficients range from 0.80 to 0.93. Temporal stability of the
nine factors with 3 months apart ranged from 0.71 to 0.90; (b)
validity—exploratory factor analysis and construct validity
were used to validate the motivational factors which emerged in
Masters et al.’s study.
12
Convergent and discriminant validity of the MOMS were
demonstrated by correlating “competitiveness” with mara-
thon’s finish time and “training miles” per week as well as with
the “win” and “competition” goal orientations. Personal goal
achievement motives were negatively correlated with current
and previous finish times. Goal achievement motives were posi-
tively related to training miles. Personal goal achievement
accounted for approximately four times as much as the variance
of the Goal and Competitiveness Sport Orientation Question-
naire (SOQ) scales as it did on the Win scales. According to the
authors this is an evidence for the construct validity of this
scale. Finally the affiliation motive scales were positively cor-
related to the number of times the runners met with their peers
and negatively with occasions of training alone. The psycho-
logical coping motives were positively correlated with disso-
ciative attention strategies during the run. The weight concern
motive was positively correlated with body mass index (BMI),
endorsing weight concerns and less body satisfaction, feeling
heavier and reporting for burning calories. The MOMS was not
correlated with social desirability showing satisfactory dis-
criminant validity.
2.3. Procedure
Participants were recruited by the following means: (1)
Names were taken from three online marathon running
listserves. Listserve administrators were initially contacted in
order to obtain permission to post recruitment information on
the website. After permission was granted, an announcement
explaining the study purpose was posted to the listserves; (2) A
post was published on the researcher’s Facebook wall, asking
friends to participate in the procedure of “a friend brings a
friend”; (3) Different running groups were approached, and an
e-mail message was sent to the group coach; (4) A research
assistant arrived at the annual half-marathon—“Brooks-
Marathonia” event, where many marathon runners were
expected to participate, and distributed questionnaires to
runners who agreed to participate in the study. All participants
in the current study were provided with information about the
study topic. Participants who chose to participate online clicked
on a link to the questionnaire and anonymously answered
through the Google Docs system. Questionnaire completion
time was approximately 10 min.
2.4. Data analysis
The analyses pertaining to the MOMS are presented in
several subsequent stages as follows:
(a) The validation of the new model was based on a system-
atic series of procedures, as recommended previously by
others.
30,31
We first tested the fit of the general model
reported by Masters et al.
12
to our data via confirmatory
factor analysis (CFA) using Muthén and Muthén’s
31
Mplus
(version 2.01 software). If the model fit the data, a
cross-cultural validation was evident. Fit statistics
followed recommendations.
32
Cutoff criteria for
fit indices were: χ
2
= non-significant (p > 0.05),
χ
2
/df > 2.00, RMSEA < 0.08, CFI > 0.90, TLI > 0.90, and
SRMR < 0.08.
(b) An exploratory factor analysis (EFA) was performed to
test the best factorial solution for the current data. A
subsequent CFA was performed to test the fit of the
revised factorial structure to the data. If the fit was satis-
factory, a new cultural-dependent structure would be
evident.
(c) A series of EFAs using the maximum likelihood factor
extraction method were performed to explore the best
factorial structure produced by the data. The best struc-
ture solution was tested for fit to the data, and reported
herein.
3. Results
3.1. Testing the original model of Masters et al.
12
The MOMS consists of 56 items which load on nine latent
factors: nine on psychological coping (PC), eight on self-
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3Motivation dimension for running a marathon
esteem (SE), and seven on life meaning (LM), which together
constitute a second-order latent factor termed psychological
motives (PM). Similarly, six items load on a latent factor
termed general health orientation (GHO) and four items load on
weight concern (WC), which together constitute a second-order
latent factor termed physical health motives (PHM). Next, six
items load on affiliation (AFF) and six on recognition (REC),
which together define a second-order latent factor termed social
motives (SM). Finally four items load on competition (COM)
and six on personal goal achievement (PGA), which together
constitute achievement motives (AM). According to Ogles and
Masters
11
the four second-order motives were moderately to
strongly correlated; specifically, psychological and social
motives (r = 0.74), psychological and physical health motives
(r = 0.67), and social and achievement motives (r = 0.65).
Furthermore the authors claimed a sufficient fit of this model
to the data, χ
2
= 6808.12, df = 1460, p < 0.001, χ
2
/df = 4.66,
Goodness of Fit Index (GFI) = 0.641, RMSEA =0.088. These fit
indices cannot be considered satisfactory in order to justify the
marathon motivation structure as claimed by its developers. The
CFA fit statistics of the original factor structure for the current
sample were χ
2
= 4858.67, df = 1469, χ
2
/df = 4.66, p < 0.001,
RMSEA = 0.088, CFI = 0.726, TLI = 0.712, SRMR = 0.107,
indicating that the original MOMS structure does not suffi-
ciently fit the current data, and therefore additional exploration
is required to attain a better fitting solution.
3.2. EFA
EFA performed on the current data using a principal com-
ponent procedure followed by oblimin rotation with eigenvalue
λ > 0.30 revealed seven factors instead of the nine original
ones, and 37 items with loadings >0.30 instead of the original
56 items. Particularly LM, but also PGA, GHO, and PC were
rated higher as motives (mean = 4.18–4.74 on a 7-point scale)
than AFF, WC, and REC (mean = 3.15–3.86). Cronbach’s α
coefficients were satisfactory and ranged from 0.83 to 0.93.
This analysis is summarized in
Table 1. The correlations among
the newly emerged factors are presented in Table 2. The corre-
lations among these factors were low to moderate (r = 0.09–
0.57), indicating sufficient inter-independence among the seven
factors. However, the CFA failed to confirm this reduced model
(χ
2
= 1934.11, df = 608, p < 0.001, RMSEA = 0.09, CFI = 0.82,
TLI = 0.81, SRMR for uncorrelated residuals; or χ
2
= 1608.37,
df = 605, p < 0.001, RMSEA = 0.07, CFI = 0.87, TLI = 0.85,
SRMR = 0.07 for correlated residuals). Thus, a new solution
was explored using EFA with maximum likelihood extraction
method.
3.3. EFA with maximum likelihood extraction method
EFA with maximum likelihood extraction method was
implemented by using Mplus 7.0.
30
Because the latent factors of
the model were found to be moderately correlated in our analy-
sis as well as in Masters et al.’s
12
study, an oblique Geomin
rotation was applied to determine the best factorial solution.
The full information maximum likelihood estimation (FIML)
method was used for parameter estimation, as this method is the
best for working with missing data.
33
A sample correlation
matrix was the matrix of association by Mplus 7.0 (Appendix).
To determine the number of factors to retain, we assumed λ > 1
rule, scree test and fit indices provided by Mplus, which
includes RMSEA, CFI, TLI, and SRMR, and can be interpreted
similarly to the fit indices in CFA.
Accordingly, the number of factors to retain was 11, as
indicated by the 11 λ at 1.1 and the scree test, as well as the
more appropriate fit statistics than any of the other 5–10
factor solutions.
Table 3 shows the model fit indices of all the
solutions, revealing that the EFA model with 11 factors
retained the best model fit statistics (χ
2
= 2088.99, df = 979,
χ
2
/df = 2.13, RMSEA = 0.062, CFI = 0.910, TLI = 0.859, and
SRMR = 0.024). The correlations among the 11 factors indi-
cate low to moderate correlations among the factors and a
satisfactory independence solution (
Table 4). The final model
is shown in
Fig. 1. The factor loadings are derived from the
Geomin rotated procedure.
Table 1
Initial exploratory factor analysis (EFA) solution yielding seven factors with means, SDs, and internal consistencies.
Factor No. of items Items λ > 0.30 Mean SD Cronbach’s α
Life meaning (LM) 3 13, 20, 25 4.74 1.54 0.83
Personal goal achievement (PGA) 9 2, 5, 9, 22, 35, 40, 43, 46, 52 4.32 1.18 0.87
General health orientation (GHO) 4 8, 14, 26, 44 4.29 1.63 0.88
Psychological coping (PC) 7 18, 28, 36, 38, 39, 47, 50 4.18 1.50 0.91
Affiliation (AFF) 6 7, 12, 16, 24, 30, 33 3.26 1.40 0.89
Weight concerns (WC) 3 1, 4, 21 3.86 1.83 0.93
Recognition (REC) 5 3, 19, 45, 48, 54 3.15 1.45 0.90
Table 2
Pearson product moment correlations among the MOMS’ seven factors.
Variable LM GHO AFF REC PC WC
GHO 0.27
**
AFF 0.50** 0.30**
REC 0.44** 0.17** 0.57**
PC 0.56** 0.38** 0.43** 0.37**
WC 0.15** 0.37** 0.22** 0.32** 0.28**
PGA 0.26** 0.09 0.32** 0.50** 0.33** 0.15*
Abbreviations: LM = life meaning; GHO = general health orientation;
REC = recognition; PC = psychological coping; WC = weight concerns;
AFF = affiliation; PGA = personal goal achievement. *p < 0.05; **p < 0.01.
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4 S. Zach et al.
The 11-factor model solution, which fits the data best, makes
several changes to the original model.
12
Specifically, the PC
unitary factor was broken down into two factors: PC1,
which pertains to emotional-related coping, and PC2, which
pertains to motives related to everyday-life management. SE
came out as a new factor with four items, which failed to define
it as such in the original model. The original SE items were
distributed among the other current factors. The factor GHO
was divided here into GHO1—reduction in disease prevalence,
and GHO2—keeping fit. Overall, the original model remained
conceptually similar to the model reported herein, but the
current model is psychologically sounder and fits better to the
data.
A further CFA analysis was implemented to test the model
described in
Fig. 1. Based on modification indices, error corre-
lations between items 3 with 6; 5 with 22; 36 with 38; and 49
with 50 were added. Results show unsatisfactory fit indices
(χ
2
= 3703.276, df = 1422, χ
2
/df = 2.60, RMSEA = 0.073,
CFI = 0.815, TLI = 0.800, and SRMR = 0.101). Second-order
CFA model fit (e.g., similar to the original model) yielded
indices of χ
2
= 4042.251, df = 1460, χ
2
/df = 2.77, RMSEA =
0.077, CFI = 0.791, TLI = 0.780, and SRMR = 0.116. Thus, the
11-factor independent model presented in
Fig. 1 is the best fit to
the data.
4. Discussion
The purpose of this study was to examine evidence for a
cross-cultural validation of the MOMS model of marathon run
motivation,
12
and to explore the best factorial structure solution
associated with the data. The study presented findings related to
validation of a new model for the MOMS. The psychometric
soundness of the new MOMS model not only exceeds the
former model, but also essentially proves what the former failed
to do. The poor fit of the original model demonstrated that the
nature of motives for running a marathon is not hierarchically
oriented with first-order and second-order factors. We showed
that, conversely, all factors could be better viewed as indepen-
dent factors.
The validation of the new model was based on a systematic
series of procedures, as recommended previously by others,
30,31
involving CFA and EFA, in which a cross-cultural validation
was not evident. A series of EFAs using the maximum likeli-
hood factor extraction method found the best factorial structure
solution representing marathoners’ motives. In other words, the
present results indicate that the Masters et al’s.
12
model cannot
be supported.
The new model distinguished an additional two factors from
the same 56 original items. Two factors emerged for psycho-
logical coping: emotional-related coping and everyday-life
management, whereas only one factor appeared in the original
model. Two factors emerged for general health orientation:
reduced disease prevalence and longevity and keep fit, whereas
only one factor appeared in the original model. We maintain
that such an addition gives a better perspective, with meaning-
ful distinctions, on running motives.
Since we could not provide additional evidence for the con-
struct validity of the original MOMS, we suggest using the new
version of the model, which explains the construct validity of
the questionnaire, in order to enhance understanding the nature
of motivation for running a marathon.
The last major argument in favor of the current model is that
while the former was not based on any theoretical framework,
the new emergent model is grounded on SDT. Along the lines of
SDT with its three-needs argument, we claim that the additional
factors that emerged in the current model represent the need for
autonomy and competence more comprehensively. As such, we
join others who maintained that satisfaction of these needs
produces self-determined motivational consequences.
17,18
Spe-
cifically, individuals who are motivated to emotionally cope
Table 3
Fit indices for exploratory factor analysis (EFA) with 5–11 factors.
No. of factors χ
2
df χ
2
/df CFI TLI SRMR 11
5 4743.66 1270 3.74 0.10 0.72 0.66 0.05
6 4009.11 1219 3.29 0.09 0.77 0.71 0.05
7 3396.82 1169 2.91 0.08 0.82 0.76 0.04
8 2978.49 1120 2.66 0.07 0.85 0.79 0.03
9 2700.19 1072 2.52 0.07 0.87 0.81 0.03
10 2358.18 1025 2.30 0.07 0.89 0.84 0.03
11 2088.19 979 2.13 0.06 0.91 0.86 0.02
Table 4
Factor correlation matrix after Geomin rotation.
PC1 PC2 LM SE REC AFF WC GHO1 GHO2 COM
PC2 0.36
LM 0.33 0.50
SE 0.27 0.23 0.20
REC 0.30 0.18 0.31 0.36
AFF 0.27 0.34 0.40 0.27 0.38
WC 0.19 0.23 0.14 0.17 0.29 0.16
GHO1 0.15 0.28 0.22 0.12 0.00 0.18 0.34
GHO2 0.07 0.23 0.22 −0.04 0.02 0.13 0.05 0.14
COM 0.12 0.16 0.04 0.14 0.23 0.15 0.14 0.02 0.08
PGA 0.17 0.21 0.30 0.13 0.27 0.09 −0.05 0.01 0.26 0.22
Abbreviations: PC1 = psychological coping—emotional-related coping—; PC2 = psychological coping—everyday-life management; LM = life meaning; SE = self
esteem; REC = recognition; AFF = affiliation; WC = weight concerns; GHO1 = general health orientation—reduced disease prevalence and longevity;
GHO2 = general health orientation—keep fit; COM = competition; PGA = personal goal achievement.
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5Motivation dimension for running a marathon
with challenges, and strive to manage their lives while adopting
running as an ongoing habit, are situated in a high self-
determined motivation state along the continuum. In addition,
the emergence of two factors related to general health orienta-
tion reflects two distinct aspects: one regarding prevention or
reduction of health problems, and the other regarding health
promotion—two motives that can satisfy the need for compe-
tence and autonomy.
Looking deeper into the content of 56 items represented by
11 factors, we found that the new model provides a detailed
description of people’s motives to run that thoroughly repre-
sents the three needs postulated by SDT. Life meaning, self-
esteem, weight concerns, and general health orientation aimed
at reducing disease prevalence and enhancing longevity,
general health orientation—keeping fit; all can refer to the need
for autonomy. Psychological coping—emotional-related and
everyday-life management can refer to the need for compe-
tence. Recognition and affiliation can refer to the need for
relatedness.
Along the lines of SDT, people’s motivation behavior varies
in the extent to which their regulation is self-determined. There-
fore, we found not only a variety of motives for running, but
also a different self-determined level that set the running behav-
ior. Specifically, competition and personal goal achievement
represent motives that stem from the need for competence.
Psychological coping—emotional-related, and everyday life
management, life meaning, self-esteem, weight concerns, and
general health orientation aimed at disease reduction, preva-
lence and longevity, and keeping fit represent motives that stem
from the need for autonomy. Finally, recognition and affiliation
represent motives that stem from the need for relatedness. Thus,
our findings support the SDT premise that satisfying the indi-
vidual’s needs satisfaction is the underlying mechanism that
drives marathon runners.
16
Although this study provided preliminary evidence of the
validity of the MOMS new model, we recommended that its
psychometric properties should be tested in other cultures,
and then be used to compare the motives of leisure vs. com-
petitive athletes, male vs. female athletes, experienced vs.
unexperienced athletes, as well as any cross-sectional criteria
deemed of interest. Such knowledge can promote understand-
ing of the complex dynamics involved in such demanding
challenges as the marathon adherence-exercise behavior. In
addition, it should be noted that we tested the factorial valid-
ity of the MOMS without changing, adding, or revising items.
It is likely that other motivational factors will be uncovered in
the future.
From an academic viewpoint, characterization of the
motives for running marathons can spur further research into
the growing trend toward participation in long runs, elicit gen-
eralizations pertaining to other endeavors, and serve as the basis
for more complex studies in terms of the number and essence of
variables. For these purposes, the instrument validated in this
study can be useful.
A longitudinal study would enable researchers to identify
motivational trends for marathon running over time, which can
be compared to trends of engagement in other sports or
Fig. 1. Final model structure and standardized item loadings.
PC1 = psychological coping—emotional-related coping; PC2 = psychological
coping—everyday-life management; LM =life meaning; SE = self esteem;
REC = recognition; AFF = affiliation; WC = weight concerns; GHO1 = general
health orientation—reduced disease prevalence and longevity; GHO2 = general
health orientation—keep fit; COM = competition; PGA = personal goal
achievement.
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6 S. Zach et al.
examined in relation to various epidemiological or sociological
phenomena.
For coaches in the field and other interested parties, an
understanding of motivation can help channel runners into
frameworks most suited to their needs and motivation, includ-
ing factors such as age, gender, and personal characteristics. It
may also be possible to predict and/or influence the decision by
participants to drop out.
Coaches, coordinators, and sport associations can use data
about motivation to tailor programs that cater to specific needs,
such as marathon running for a healthy lifestyle, social mara-
thon running, running to realize personal potential, and running
for achievement, and to direct each approach to relevant target
populations. Also, marketers and sports event organizers can
use the knowledge regarding participants’ motives and expec-
tations in their plans and decisions.
Appendix
Correlation matrix for the input association matrix of EFA (Part 1)
Item 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
02 0.15
03 0.27 0.45
04 0.82 0.18 0.32
05 0.17 0.48 0.29 0.18
06 0.27 0.35 0.87 0.32 0.28
07 0.10 0.23 0.37 0.15 0.29 0.41
08 0.37 0.02 0.03 0.32 0.009 0.03 0.17
09 −0.03 0.26 0.20 −0.01 0.032 0.24 0.15 0.04
10 0.14 0.13 0.23 0.16 0.16 0.25 0.29 0.21 0.17
11 0.13 0.22 0.44 0.19 0.19 0.48 0.30 0.12 0.33 0.54
12 0.11 0.09 0.35 0.20 0.11 0.40 0.57 0.19 0.15 0.37 0.42
13 0.02 0.07 0.24 0.08 0.07 0.29 0.34 0.11 0.21 0.26 0.49 0.49
14 0.23 0.08 0.02 0.25 0.09 0.05 0.17 0.58 −0.01 0.22 0.16 0.21 0.27
15 0.21 0.14 0.16 0.26 0.14 0.22 0.19 0.20 0.18 0.52 0.45 0.32 0.42 0.34
16 0.14 0.14 0.29 0.19 0.12 0.31 0.74 0.22 0.10 0.27 0.33 0.64 0.44 0.26 0.37
17 0.12 0.08 0.03 0.04 0.25 0.08 0.21 0.33 0.18 −0.02 0.08 0.11 0.20 0.30 0.14 0.22
18 0.21 0.08 0.20 0.24 0.15 0.26 0.28 0.29 0.22 0.37 0.32 0.34 0.38 0.22 0.55 0.37 0.28
19 0.16 0.26 0.61 0.27 0.15 0.67 0.38 0.12 0.23 0.23 0.48 0.42 0.32 0.23 0.29 0.38 0.09 0.31
20 0.08 0.07 0.27 0.12 0.12 0.31 0.29 0.15 0.25 0.25 0.49 0.41 0.77 0.23 0.45 0.41 0.20 0.43 0.43
21 0.80 0.14 0.31 0.80 0.016 0.34 0.12 0.27 0.05 0.17 0.18 0.22 0.13 0.26 0.28 0.20 0.08 0.28 0.28 0.19
22 0.06 0.45 0.21 0.09 0.077 0.21 0.20 0.02 0.49 0.10 0.19 0.07 0.06 0.07 0.12 0.06 0.33 0.17 0.18 0.18 0.13
23 0.17 0.26 0.47 0.22 0.25 0.53 0.37 0.12 0.28 0.48 0.75 0.47 0.48 0.15 0.49 0.42 0.18 0.42 0.48 0.48 0.26 0.27
24 0.14 0.18 0.26 0.21 0.17 0.30 0.44 0.24 0.11 0.30 0.29 0.51 0.29 0.27 0.25 0.47 0.12 0.28 0.37 0.34 0.22 0.16 0.38
25 0.09 0.12 0.22 0.17 0.16 0.29 0.19 0.15 0.19 0.29 0.51 0.30 0.55 0.21 0.42 0.29 0.17 0.38 0.30 0.54 0.20 0.14 0.57 0.32
26 0.30 0.03 0.00 0.35 0.11 0.04 0.11 0.57 −0.05 0.24 0.15 0.16 0.17 0.66 0.31 0.21 0.25 0.30 0.18 0.24 0.31 0.08 0.14 0.30 0.25
Correlation matrix for the input association matrix of EFA (Part 2)
Item 1 2 3 4 5 6 7 8 9 1011121314 15 161718192021 2223242526
27 0.13 0.08 0.21 0.20 0.18 0.27 0.25 0.19 0.20 0.31 0.45 0.31 0.67 0.26 0.48 0.39 0.24 0.48 0.27 0.70 0.25 0.15 0.52 0.32 0.71 0.35
28 0.17 0.11 0.20 0.16 0.14 0.27 0.20 0.29 0.31 0.33 0.47 0.30 0.53 0.24 0.58 0.36 0.27 0.57 0.28 0.56 0.24 0.17 0.51 0.21 0.54 0.27
29 0.12 0.13 0.39 0.18 0.12 0.47 0.25 0.12 0.28 0.40 0.73 0.37 0.58 0.21 0.53 0.35 0.19 0.43 0.49 0.65 0.25 0.19 0.71 0.32 0.60 0.21
30 0.02 0.18 0.33 0.10 0.21 0.36 0.64 0.15 0.19 0.34 0.37 0.61 0.41 0.22 0.24 0.67 0.18 0.28 0.42 0.41 0.14 0.22 0.45 0.58 0.34 0.21
31 −0.08 0.10 0.03 −0.01 0.06 0.10 0.21 0.10 0.20 0.07 0.15 0.22 0.25 0.11 0.13 0.27 0.32 0.27 0.12 0.20 −0.01 0.14 0.22 0.13 0.30 0.11
32 0.12 0.16 0.34 0.08 0.13 0.45 0.29 0.08 0.35 0.15 0.45 0.33 0.45 0.12 0.26 0.29 0.30 0.34 0.48 0.52 0.17 0.21 0.47 0.27 0.42 0.09
33 0.19 0.23 0.30 0.024 0.18 0.32 0.51 0.19 0.09 0.28 0.24 0.52 0.35 0.26 0.37 0.64 0.14 0.33 0.38 0.39 0.27 0.15 0.40 0.51 0.31 0.23
34 −0.07 0.12 0.28 −0.02 0.11 0.35 0.21 0.08 0.38 0.12 0.41 0.21 0.40 0.17 0.17 0.15 0.32 0.25 0.33 0.39 0.02 0.20 0.35 0.13 0.36 0.04
35 0.01 0.23 0.23 −0.01 0.29 0.24 0.18 0.05 0.55 0.12 0.30 0.15 0.28 0.06 0.14 0.14 0.27 0.25 0.25 0.27 0.02 0.40 0.29 0.10 0.21 0.02
36 0.19 0.08 0.14 0.22 0.22 0.23 0.15 0.24 0.24 0.31 0.31 0.23 0.36 0.28 0.46 0.26 0.18 0.57 0.28 0.40 0.23 0.24 0.40 0.31 0.40 0.30
37 0.21 0.13 0.08 0.16 0.25 0.16 0.23 0.48 0.14 0.10 0.16 0.14 0.22 0.38 0.17 0.19 0.66 0.38 0.16 0.25 0.20 0.31 0.21 0.26 0.25 0.41
38 0.16 0.13 0.14 0.19 0.22 0.22 0.21 0.24 0.17 0.28 0.34 0.25 0.38 0.31 0.45 0.31 0.28 0.55 0.29 0.38 0.22 0.22 0.42 0.30 0.39 0.36
39 0.18 0.12 0.14 0.22 0.20 0.23 0.20 0.25 0.21 0.34 0.32 0.27 0.32 0.29 0.48 0.27 0.21 0.52 0.27 0.35 0.26 0.24 0.39 0.31 0.38 0.31
40 0.02 0.30 0.27 0.05 0.32 0.27 0.14 0.02 0.56 0.06 0.24 0.07 0.19 0.03 0.07 0.06 0.27 0.18 0.29 0.24 0.09 0.50 0.22 0.12 0.16 0.02
41 0.04 0.13 0.17 0.05 0.09 0.18 0.22 0.12 0.14 0.27 0.26 0.28 0.34 0.17 0.30 0.32 0.14 0.34 0.25 0.36 0.09 0.07 0.39 0.38 0.36 0.12
42 0.52 0.23 0.30 0.48 0.23 0.33 0.22 0.32 0.11 0.23 0.30 0.26 0.28 0.32 0.32 0.29 0.30 0.37 0.34 0.28 0.54 0.16 0.34 0.28 0.24 0.31
43 0.12 0.69 0.52 0.20 0.47 0.48 0.34 −0.02 0.28 0.17 0.29 0.24 0.17 0.12 0.16 0.25 0.09 0.17 0.44 0.16 0.21 0.47 0.39 0.29 0.16 0.08
44 0.31 0.07 0.03 0.31 0.10 0.07 0.10 0.66 −0.02 0.21 0.20 0.15 0.16 0.61 0.26 0.21 0.37 0.26 0.21 0.20 0.27 0.07 0.21 0.29 0.24 0.78
45 0.18 0.35 0.62 0.28 0.22 0.64 0.32 0.08 0.19 0.28 0.45 0.32 0.30 0.17 0.32 0.35 0.04 0.21 0.64 0.35 0.27 0.17 0.51 0.27 0.31 0.14
46 −0.05 0.37 0.26 −0.03 0.42 0.25 0.17 −0.08 0.46 0.01 0.22 0.04 0.13 −0.07 −0.04 0.05 0.26 0.04 0.19 0.13 0.00 0.53 0.19 0.10 0.14 −0.08
47 0.27 0.10 0.14 0.27 0.24 0.18 0.23 0.27 0.23 0.36 0.37 0.27 0.34 0.22 0.52 0.35 0.33 0.65 0.24 0.39 0.27 0.27 0.47 0.27 0.40 0.27
48 0.13 0.27 0.51 0.23 0.24 0.56 0.44 0.06 0.19 0.35 0.48 0.49 0.39 0.19 0.37 0.48 0.13 0.35 0.56 0.44 0.27 0.24 0.57 0.40 0.41 0.17
49 0.14 0.04 0.14 0.18 0.13 0.18 0.15 0.19 0.18 0.25 0.31 0.22 0.35 0.21 0.39 0.25 0.14 0.48 0.27 0.41 0.18 0.14 0.41 0.23 0.38 0.24
50 0.12 0.04 0.19 0.14 0.13 0.24 0.18 0.17 0.20 0.27 0.32 0.24 0.30 0.14 0.37 0.23 0.16 0.54 0.25 0.33 0.18 0.16 0.42 0.20 0.31 0.14
51 0.07 0.22 0.18 0.11 0.37 0.22 0.13 0.14 0.32 0.14 0.31 0.14 0.14 0.07 0.13 0.12 0.30 0.17 0.21 0.18 0.10 0.45 0.34 0.16 0.24 0.14
52 0.12 0.59 0.45 0.23 0.37 0.40 0.21 0.06 0.16 0.23 0.32 0.19 0.11 0.18 0.23 0.23 0.08 0.17 0.45 0.19 0.23 0.34 0.40 0.32 0.21 0.18
53 0.08 0.17 0.24 0.06 0.15 0.27 0.17 0.16 0.31 0.18 0.42 0.19 0.26 0.16 0.20 0.21 0.26 0.19 0.27 0.29 0.08 0.22 0.41 0.20 0.32 0.10
54 0.21 0.32 0.75 0.29 0.23 0.77 0.42 0.04 0.21 0.24 0.48 0.44 0.29 0.11 0.24 0.41 0.09 0.22 0.67 0.35 0.31 0.21 0.55 0.37 0.27 0.07
55 0.15 0.17 0.28 0.19 0.11 0.30 0.26 0.20 0.10 0.36 0.36 0.32 0.33 0.31 0.49 0.40 0.06 0.43 0.34 0.36 0.21 0.09 0.43 0.35 0.43 0.27
56 0.13 0.36 0.41 0.18 0.24 0.42 0.25 0.08 0.39 0.19 0.45 0.20 0.32 0.21 0.25 0.25 0.13 0.23 0.46 0.37 0.16 0.35 0.44 0.21 0.34 0.20
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7Motivation dimension for running a marathon
Correlation matrix for the input association matrix of EFA (Part 3)
Item 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
28 0.62
29 0.62 0.67
30 0.38 0.27 0.42
31 0.29 0.30 0.19 0.25
32 0.46 0.45 0.57 0.39 0.34
33 0.40 0.31 0.32 0.62 0.16 0.25
34 0.38 0.38 0.44 0.28 0.40 0.64 0.15
35 0.23 0.28 0.27 0.24 0.25 0.47 0.11 0.54
36 0.52 0.55 0.43 0.29 0.29 0.31 0.38 0.28 0.28
37 0.32 0.31 0.23 0.19 0.38 0.32 0.19 0.28 0.20 0.29
38 0.52 0.53 0.45 0.31 0.34 0.32 0.38 0.27 0.22 0.85 0.35
39 0.44 0.52 0.42 0.32 0.25 0.31 0.42 0.24 0.20 0.73 0.32 0.76
40 0.22 0.21 0.23 0.19 0.24 0.43 0.11 0.54 0.69 0.24 0.23 0.20 0.19
41 0.43 0.39 0.36 0.31 0.22 0.24 0.40 0.18 0.19 0.44 0.21 0.45 0.48 0.16
42 0.35 0.32 0.34 0.28 0.22 0.33 0.32 0.16 0.16 0.24 0.43 0.31 0.30 0.16 0.32
43 0.20 0.16 0.24 0.35 0.10 0.26 0.39 0.21 0.28 0.19 0.18 0.23 0.20 0.35 0.23 0.28
44 0.32 0.28 0.24 0.22 0.17 0.18 0.27 0.12 0.06 0.29 0.52 0.36 0.35 0.05 0.20 0.46 0.13
45 0.32 0.28 0.45 0.39 0.07 0.36 0.41 0.27 0.27 0.18 0.12 0.21 0.25 0.22 0.28 0.37 0.54 0.25
46 0.09 0.07 0.18 0.22 0.24 0.31 0.06 0.40 0.42 0.08 0.17 0.12 0.14 0.56 0.05 0.16 0.39 0.00 0.20
47 0.48 0.60 0.46 0.27 0.33 0.31 0.39 0.21 0.24 0.64 0.43 0.64 0.62 0.20 0.42 0.37 0.28 0.34 0.25 0.13
48 0.41 0.35 0.49 0.53 0.15 0.38 0.53 0.30 0.25 0.33 0.20 0.38 0.39 0.18 0.35 0.35 0.52 0.20 0.70 0.24 0.44
49 0.49 0.45 0.37 0.24 0.23 0.28 0.31 0.21 0.20 0.68 0.24 0.67 0.57 0.16 0.54 0.31 0.20 0.27 0.26 0.05 0.60 0.40
50 0.41 0.46 0.36 0.27 0.26 0.29 0.32 0.24 0.23 0.62 0.21 0.58 0.54 0.18 0.46 0.29 0.22 0.19 0.26 0.10 0.58 0.41 0.83
51 0.21 0.21 0.28 0.23 0.25 0.32 0.12 0.35 0.43 0.20 0.34 0.23 0.22 0.43 0.21 0.31 0.29 0.23 0.24 0.51 0.33 0.27 0.26 0.30
52 0.17 0.17 0.28 0.34 0.10 0.22 0.40 0.15 0.19 0.24 0.13 0.26 0.29 0.25 0.33 0.28 0.73 0.24 0.54 0.34 0.31 0.51 0.25 0.27 0.35
53 0.29 0.31 0.40 0.26 0.28 0.41 0.19 0.46 0.46 0.24 0.29 0.27 0.25 0.39 0.36 0.33 0.24 0.28 0.30 0.34 0.36 0.31 0.23 0.27 0.54 0.31
54 0.26 0.25 0.48 0.46 0.09 0.44 0.40 0.30 0.29 0.20 0.14 0.22 0.23 0.23 0.25 0.39 0.54 0.15 0.75 0.24 0.25 0.69 0.22 0.25 0.26 0.53 0.33
55 0.51 0.43 0.42 0.35 0.13 0.25 0.51 0.16 0.09 0.51 0.15 0.53 0.55 0.05 0.54 0.31 0.29 0.28 0.41 −0.04 0.48 0.47 0.55 0.52 0.14 0.39 0.28 0.40
56 0.37 0.35 0.46 0.33 0.22 0.47 0.22 0.47 0.44 0.24 0.17 0.27 0.26 0.41 0.20 0.28 0.44 0.24 0.47 0.33 0.32 0.39 0.25 0.26 0.36 0.40 0.53 0.50 0.37
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ARTICLE IN PRESS
JSHS223_proof 8 January 2016 9/9
Please cite this article in press as: Sima Zach, et al., Motivation dimensions for running a marathon: A new model emerging from the Motivation of Marathon Scale (MOMS), Journal
of Sport and Health Science (2015), doi: 10.1016/j.jshs.2015.10.003
9Motivation dimension for running a marathon
... It is assumed that outdoor competitions will resume faster than indoor, and this is especially true for competitions held in nature, that is, outside the stadiums. Given this, running events, which are very popular all over the world (Lee et al., 2017;Nowak, 2015;Zach et al., 2017;Scheer et al., 2020), might be a reasonable option to start with. Still, the attitudes of people belonging to the running community towards their participation in post covid-19 running events and towards their perception of safety protective measures have not been analysed to date. ...
... Race runners share similar motives for participation. The motives of marathon runners have been widely explored and motives are mainly categorized as psychological (maintaining or enhancing self-esteem, coping with negative emotions), social (sense of affiliation and receiving recognition or approval from others), physical (general health, weight concern), and achievement (competition with other runners and personal goal achievement) (Masters et al., 1993;Deaner et al., 2011;Zach et al., 2017). The motivations of runners have been further analysed in different sport event contexts (i.e. ...
... Furthermore, although running is often perceived as an individual sport (Masters et al., 1993;Deaner et al., 2011;Zach et al., 2017), this study found that almost 83 of the respondents will not travel and visit the event alone. This indicates that people missed social gatherings during the pandemic. ...
... Researchers have found many facts that achievement motivation plays a very important role in obtaining the best performance (Anderman, 2020;Lochbaum et al., 2022). Achievement motivation affects one's competence (Zach et al., 2017). Achievements motivationis urgently are needed when doing any activity, and behavior can be considered motivational when it involves competition with standards of excellence (Brunstein & Heckhausen, 2018). ...
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Sports achievement is influenced by various factors such as; physical, technical, tactical and psychological factor. The purpose of this study is to see the relation of self-efficacy factors, achievement motivation, and its relation to self-confidence and its effect on sports performance. This research was conducted on 87 students. This is quantatif research and using path analysis method. The sample was taken by technique purposive random samplin and the sample was 45 people with certain considerations. The data of self-efficacy, achievement motivation, and self-confidence was taken by using valid and reliable quistionnaire, and the sport performance data taken from the performance (medal) are already gotten. For testing the structural effects of the model from this study, researchers used IBM SPSS software. The results show that self efficacy, achievement. motivation, and self-confidence have a significant influence on the athletes' sports performance inndividual sports both of directly and indirectly, or totally. The implications of the theory obtained reinforce that the variables tested have a direct impact toward athletes achievement of the Student Sport Training Center in West Sumatera Province. The limitation of this study is the instrument that needs to be developed more accurately and comprehensively to reveal psychological conditions.
... Many amateur sportsmen declare that they train for the sake of training, while others are motivated by competition [81]. In real-life amateur sports, most participants do not compete to win. ...
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Physical activity is entering the virtual realm. Zwift is an at-home cycling system that is enjoying increasing popularity, yet the specifics of the experience of a virtual cyclist have not been studied to date. Building virtual sports systems can make physical activity accessible to more diverse user groups. To understand how and why users engage in virtual cycling, we conducted n=22 interviews with Zwift users. Through charting the motivations behind using Zwift, we determined that it allowed users to engage in a range of cycling activities traditionally reserved for professional cyclists. Our work reports on key motivations and identifies five key strategies which Zwift uses to create an engaging virtual sports experience. Further, we discuss how Zwift creates a world of virtual professionalism. Our findings offer a structured understanding of the experience of Zwift which can be used to inspire the design of future virtual amateur sports systems.
... Marathon is a mass sports event attracting people to take part in regardless of the races, incomes, regions, classes. The motivation of people to take part in marathon events could be distinct, while solving health problems stands for physical motive, making friends stands for social motives, and discovering the life meaning represents the psychological ones as well as self-achievement [4] [5]. However, due to the pandemic of COVID-19, the world marathon majors (including Boston, Chicago, New York, Berlin, London, Tokyo) were postponed or canceled in 2020, and the London marathon was the only one open for public participants while the Tokyo marathon is limited to elites for Olympic trail (official Abbott marathon majors net) [6]. ...
... The motivations for people to take on this huge effort are manifold. They can be of personal (goal achievement), social (respect of peers), physical (lose weight) and psychological (becoming less anxious) manner [197]. Independent of the motives behind the decision to participate in a marathon, all those runners are united in the task to prepare well by bringing their bodies in shape to run 42.2 km. ...
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Body-worn sensors, so-called wearables, are getting more and more popular in the sports domain. Wearables offer real-time feedback to athletes on technique and performance, while researchers can generate insights into the biomechanics and sports physiology of the athletes in real-world sports environments outside of laboratories. One of the first sports disciplines, where many athletes have been using wearable devices, is endurance running. With the rising popularity of smartphones, smartwatches and inertial measurement units (IMUs), many runners started to track their performance and keep a digital training diary. Due to the high number of runners worldwide, which transferred their data of wearables to online fitness platforms, large databases were created, which enable Big Data analysis of running data. This kind of analysis offers the potential to conduct longitudinal sports science studies on a larger number of participants than ever before. In this dissertation, both studies showing how to extract endurance running-related parameters from raw data of foot-mounted IMUs as well as a Big Data study with running data from a fitness platform are presented.
... 42.195 km) is no longer postulated to be a 'only for elite athletes' type of activity (Kruger and Saayman 2013). While most marathon runners are motivated by the perceived benefits of running (Loughran et al. 2013), multiple reasons, such as being healthy, maintaining psychological well-being, feeling in competition and controlling mood, have all been identified as motivation to train for and complete a marathon (Ogles and Masters 2003;Zach et al. 2017). Not surprisingly, such a long performance involves evolving feelings and mood changes (Raglin 2007). ...
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Marathon running is a physical and mental activity. Runners consume high-energy food products to fill their glycogen stores for maintaining their marathon performance. This makes consuming carbohydrates, mainly in the form of energy gels, an essential part of marathon running. While previous research demonstrates significant physiological effects of these high-energy food products on performance, their psychological effects, which could benefit from and shed light on food design studies, have been underexplored. This article explores these effects with two participant studies, a narrative study ( n = 10) and a survey ( n = 39). The inquiries start with understanding the psychology of marathon runners and examining the psychological effects of energy gels on marathon running. The results showed that the marathon runners follow a self-identified energy gel consumption strategy during marathon running. Several qualities of energy gels influence these strategies and the meanings marathon runners attach to energy gel consumption. The findings elucidated a novel area of food design research by unveiling the nature of the non-nutritional interactions between runner and energy gels consumed in marathon running.
... The inclusion criterion required that the participants had at least six months of running training with at least three weekly training sessions and had already completed at least one LDR race (10K, half marathon or full marathon). Previous studies have also used the same sampling method (Zach et al., 2017;Popov et al., 2019). ...
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In recent years, the growing number of long-distance runners in Indonesia has raised questions about the factors that motivate them. This study aimed to analyse the motivations of long-distance runners according to their gender and running experience. This research used a quantitative approach and an internet-based study. A sample consisting of 130 participants (71% were males, and 29% were females) participated in this study and voluntarily completed the survey. The inclusion criterion required that the participants had at least six months of running training with at least three weekly training and had already completed at least one long-distance running race (10K, half marathon or full marathon). In this study, the Indonesian adapted version of Motivation of Marathoners Scales by Masters et al. was used to analyse runners' motivation. Descriptive analysis, t-test, and one-way analysis of variance (ANOVA) were used to analyse the data. The obtained results revealed that runners' gender, health orientation, and personal goal achievement affected the attainment of greater motivation scores. At the same time, recognition and competition showed to have the lowest motives among the runners. There was a significant difference between male and female runners in terms of competition motive (t = 0.26, p < 0.05). According to runners' running experience, there were also significant differences in two dimensions, i.e., personal goal achievement (F = 2.76, p < 0.05) and competition (F = 2.59, p < 0.05). The most significant motivations that were considered were general health orientation, personal goal achievement, life meaning, and self-esteem; these dimensions belong to runners' physical health, achievement, and psychological motives, respectively. In contrast, the recognition and competition factors always resulted in the lowest score, which indicated that runners did not need social recognition and had low competitive motive except for competing with themselves. Future studies require higher participant involvement and evaluation of other research variables that may contribute to Indonesia's long-distance runners' motivations.
... Charity motive items were measured on a 7-point Likert-type scale from 1 = "not a reason" to 7 = "a most important reason." While runner motives have been explored in previous studies, most notably using the Motivation for Marathoners Scale (MOMS), the MOMS did not include charity motive as one of its dimensions (Masters et al., 1993;Zach et al., 2017). We included this 4-item charity motive scale because the literature on charity-related sporting events suggested that it was influential. ...
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Running, as a form of leisure time physical activity is generally popular due to its low-cost entry, easy access to practice, and the convenience and accessible nature of the activity. Specifically, one type of running experience sought by many is charitable running or running for a cause (i.e., cause-related sport event). While there is a growing body of literature on charity sport events, little is known about how the charitable motives and participant identity with the event affect future behaviors associated with the cause and the event. Grounded in identity theory, the purpose of this article was to examine the effect of salient identities and charitable motives on future intentions associated with a cause-related event. Data were collected from the second annual Norfolk Freedom Half Marathon, in Virginia, via an online survey that was sent to all registered runners (1,372) one week after the race and 557 participants responded. We found charity motives to be the dominant influence on both charitable and purchase intentions in cause-event participants. This study contributes to the existing amateur sport literature as one of the first to report on a military-oriented sport event with military affiliated participants; the creation of the Charitable Motives in Sport Scale (CMISS), the Runner Identity Scale (RIS) and the Military Identity Scale (MIS); and the addition of a new military/runner identity typology, which we hope would be useful for future military-affiliated running events.
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Marathon runners' motives vary, and differ from marathon to marathon depending on the type of race. This study determined the motives of Comrades Marathon runners in order to identify and profile the market segments competing in this ultra-marathon. Intrinsic achievement, exploration and competitiveness, family togetherness and escape, socialisation and commitment were identified as the five main motives, and from these two distinct segments were classified: recreational runners and serious runners. The research showed that the typical (real) comrade of the Comrades Marathon is a person who combines the attributes of the two clusters, serious and recreational athletes, where intrinsic achievement and commitment are key motives. The study, the first of its kind at an ultra-marathon in South Africa, fills a gap in the existing literature and contributes to the literature not only on sport events but also on marathons and ultra-marathon participants in particular. It corroborates the finding that motives for participating differ according to the sporting event, and supports the view that marketers and sports event organisers must understand that participants have different motives and so should not be regarded as a homogeneous group. This type of research is valuable to organisers, as it assists in making informed and cost-effective marketing and product development decisions.
Book
I: Background.- 1. An Introduction.- 2. Conceptualizations of Intrinsic Motivation and Self-Determination.- II: Self-Determination Theory.- 3. Cognitive Evaluation Theory: Perceived Causality and Perceived Competence.- 4. Cognitive Evaluation Theory: Interpersonal Communication and Intrapersonal Regulation.- 5. Toward an Organismic Integration Theory: Motivation and Development.- 6. Causality Orientations Theory: Personality Influences on Motivation.- III: Alternative Approaches.- 7. Operant and Attributional Theories.- 8. Information-Processing Theories.- IV: Applications and Implications.- 9. Education.- 10. Psychotherapy.- 11. Work.- 12. Sports.- References.- Author Index.
Chapter
The relationship between motivations for running a marathon and the benefits derived from its completion has not been systematically explored in the sport psychology literature. This study investigated motivations and perceived benefits of marathon participation in a non-elite population of runners. Ninety-nine runners completed questionnaires examining motivations and perceived benefits of marathon participation immediately after the event. Confirmatory factor analysis of a scale designed for the present study confirmed that the perceived benefits of marathon participation can be categorized as Psychological, Physical, and Relational. As expected, marathoners' motivations for running were predictive of perceived benefits along similar categories. Interestingly, participants also experienced perceived benefits that extended beyond their original motivations for running the marathon. The results of this study add to the body of evidence suggesting that marathon running enhances physical, psychological, and relational health.