International  Journal  of
Environmental Research
and Public Health
Article
Runner’s Perceptions of Reasons to Quit Running:
Inﬂuence of Gender, Age and
Running-Related Characteristics
Daphne Menheere 1,*, Mark Janssen 1,2
, Mathias Funk 1
, Erik van der Spek 1,
Carine Lallemand 1,3
and Steven Vos 1,2
1
Department of Industrial Design, Eindhoven University of Technology, 5600 MB Eindhoven, The
Netherlands; mark.janssen@fontys.nl (M.J.); m.funk@tue.nl (M.F.); e.v.d.spek@tue.nl (E.v.d.S.);
c.e.lallemand@tue.nl (C.L.); s.vos@tue.nl (S.V.)
2
School of Sport Studies, Fontys University of Applied Sciences, 5644 HZ Eindhoven, The Netherlands
3
HCI Research Group, University of Luxembourg, Esch-sur-Alzette, 4365 Luxembourg, Luxembourg
*
Correspondence: d.s.menheere@tue.nl
Received: 15 July 2020; Accepted: 18 August 2020; Published: 20 August 2020


Abstract: Physical inactivity has become a major public health concern and, consequently, the
awareness of striving for a healthy lifestyle has increased. As a result, the popularity of recreational
sports, such as running, has increased.
Running is known for its low threshold to start and
its attractiveness for a heterogeneous group of people. Yet, one can still observe high drop-out
rates among (novice) runners. To understand the reasons for drop-out as perceived by runners,
we investigate potential reasons to quit running among short distance runners (5 km and 10 km)
(n = 898). Data used in this study were drawn from the standardized online Eindhoven Running
Survey 2016 (ERS16). Binary logistic regressions were used to investigate the relation between reasons
to quit running and diﬀerent variables like socio-demographic variables, running habits and attitudes,
interests, and opinions (AIOs) on running. Our results indicate that, not only people of diﬀerent
gender and age show signiﬁcant diﬀerences in perceived reasons to quit running, also running habits,
(e.g., running context and frequency) and AIOs are related to perceived reasons to quit running too.
With insights into these related variables, potential drop-out reasons could help health professionals
in understanding and lowering drop-out rates among recreational runners.
Keywords: running drop-out; novice runners; gender; age; running habits; attitudes; interests; motives
1. Introduction
Physical inactivity has become a major public health concern as it is associated with the
development of chronic diseases [1,2]. Consequently, the awareness and importance of striving
for an active and healthy lifestyle within our society have increased [3]. This is notably reﬂected in the
increased popularity of unorganized recreational sports such as running [4,5]. Running is known for its
low threshold to start: it is relatively inexpensive and easy to practice [6] and is associated with many
health beneﬁts (i.e., musculoskeletal and cardiovascular health, body composition, and psychological
state) [7–14] and is therefore a popular recreational sport. This popularity is especially apparent in the
increasing number of commercial running events, and their growing number of participants. In terms
of event participation, running is even one of the most popular recreational sports in the world [15,16].
Therefore, since the begin of the 21st century, we can speak of the second wave of running [15].
The growing number and diversity of specialized running events (e.g., ladies runs, color runs,
survival runs) are aligned with the development of the heterogeneous proﬁle of ‘the runner’ over the
years [17–19]. During the ﬁrst wave of running starting in the 1960s, running used to be dominated by
Int. J. Environ. Res. Public Health 2020, 17, 6046; doi:10.3390/ijerph17176046
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young males [20,21] as it was considered outrageous for women to engage in running [22]. It was not
until almost 25 years later, the ﬁrst Olympic marathon for women was introduced [15]. This partake of
women in running continued to develop, where a strong growth is notably visible during the second
wave of running, resulting in an almost equal distribution of men and women in recent years [15,19,23].
Similar to data of other Western countries [15], 11.3% of women and 13.2% of men within Dutch
adults (i.e., the context of the present study), between the ages 20–79 years, expressed to run at least
monthly in 2012 [23], also indicating the age diversity of running participants [15,24]. Besides some
socio-demographic characteristics (i.e., gender and age) representing the heterogeneous population of
runners, studies showed a variety in terms of motives to partake in running (e.g., health, social and
competition elements, performance) [4,25,26]. Furthermore, one can also observe a broader range of
diﬀerent experienced runners (e.g., recreational, competitive) [27] but also running context (e.g., small
groups, running partner, individually) [4,19,28]. This diverse proﬁle of ‘the runner’ illustrates that
running can appeal to many people (regardless of age, gender, motives, experience or running context)
and illustrates the potential of making running even more accessible for an even larger group of people.
Despite the increasing popularity and the growing heterogeneity in runners, one can observe high
drop-out rates due to running-related injuries and motivational loss, which is often noticeable among
novice runners [29–31]. What type of runners are aﬀected by running-related injuries and how this
aﬀects a potential drop-out, and how long this drop-out lasts, has been studied extensively in previous
literature [29–33]. Although there is evidence on motivations to partake in running [17,25,34,35],
reasons to quit running are rather unexplored.
Previous studies on reasons to start running, show the inﬂuence of the diﬀerent type of
characteristics. Indicating the inﬂuence of socio-demographic variables (i.e., gender and age), running
habits (e.g., experience, frequency, relative performance) and in the runners’ attitudes, interests and
opinions (AIOs). In a study of Hanson et al. women seemed to be more motivated by AIOs on
weight concern, self-esteem, aﬃliation and psychological coping compared to men and less by AIOs
with regards to competition and goal achievement [36]. This is in line with a study by Deaner et al.,
indicating men reported higher levels of competitiveness compared to women [37]. Motivational
diﬀerences in age were investigated by Ogles and Masters, indicating young marathon participants
(20–28 years) were more motivated by personal goal achievements, compared to older marathon
runners (≥50 years). Furthermore, the older participants were more motivated by weight concerns,
life meaning, health orientation and aﬃliation. Besides gender and age, running experience also
impacts AIOs towards running. For example, Forsberg et al. showed that more experienced runners,
those who run for more than eight years, were more likely to run for social motives and just ‘for the
love of running’. Whereas lesser experienced runners, those who run up to three years were more
health orientated.
Although motives for running can inﬂuence running drop-out [38–40], to the best of our knowledge,
there is limited evidence about reasons to quit running. An important step toward expanding the
evidence base is to understand the reasons for drop-out as perceived by runners. Hence, the scope
of this paper is on the perceived reasons to quit running. Janssen et al. distinguish two groups of
perceived reasons to quit running: individual (e.g., time management, injuries) and social (e.g., running
partner/trainer quits) [4,41]. These reasons are covered by the items of the Leuven Running Survey
2009 [42] and adapted to event runners. Whether these are related to socio-demographic characteristics
as gender and age, as they are for motives to running [17,36,37], or running-related characteristics is,
however unknown.
With the present study, we aimed to: (i) gain insights in perceived reasons to quit running, and (ii)
how this is aﬀected by socio-demographics (i.e., gender and age) running habits, and AIOs on running.
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2. Materials and Methods
2.1. Study Design and Respondents
The data used in this study were drawn from the Eindhoven Running Survey 2016 (ERS2016).
We collected data through an online standardized questionnaire among runners at the Eindhoven
Marathon Running Event, which oﬀered races at 5, 10, 21.1 and 42.2 km. For this paper, a sub-dataset
was drawn with only those runners that participated in the 5 and 10 km races. These distances
were selected because of the heterogeneity of the participants, including both more experienced,
less and unexperienced runners. The items used in this questionnaire were directly derived from the
standardized questionnaire from previous editions of this event (ERS2014 and ERS2015).
In total, 18,261 runners participated in this event, who agreed upon registration that they could
be contacted for research purposes. After ﬁnishing the event, all runners received an email with an
explanation of the study, informed consent and our guarantee that their data would be processed
anonymously. If they agreed upon participation in this study, they could click the link to the online
questionnaire. The email contained all needed information and was in line with the ethical principles
of the Declaration of Helsinki and the American Psychological Association. Thereby, the Research
Board of the Fontys School of Sport Studies was consulted prior to initiation of this study, and approval
for the study design was obtained.
Of the 18,261 runners, 3727 runners completed the questionnaire (overall response rate of 20.4%)
of which 7.9% in the 5 km and 16.2% in the 10 km run. Since this study focused on the 5 and 10 km
distances, the subset used here consists of 898 runners (603 who ran the 10 km and 295 the 5 km).
The average age of the runners in the present study was 40.7 years, with the youngest runner at
18 years and the oldest 78 years old. 52.7% per cent of the participants were women (n = 474 runners).
These socio-demographic backgrounds are comparable to other running samples in previous large-scale
running studies in Western Europe [4,15,43].
2.2. Questionnaire
The online questionnaire consisted of three sections.
The ﬁrst section included attitudes,
interests, and opinions (AIOs) on running, the second focused on socio-demographics and the
last on running habits. The questionnaire is provided in the Supplementary Materials of a previous
study of Janssen et al. (File S1, questionnaire ERS2016) [41], in which Figure S1 shows a ﬂowchart of
the questionnaire.
The ﬁrst section of the questionnaire consists of items on running AIOs and was adopted from
previous studies [4,19,41,43]. Runners were asked to rate the extent to which they agreed with the items,
using a 5-point Likert scale (ranging from 1 = totally disagree, to 5 = totally agree). The second section
of the questionnaire includes questions on sociodemographic characteristics. We asked for gender
(male/female); age (years); professional status (student/unemployed/employed part-time/employed
full-time); and level of education (lower and middle/higher/university). The third section covered
running habits included running frequency (number of runs per week) years of running experience
(<1 year: novice; 1–5 years: moderately experienced; >5 years: experienced); and preferred running
context (individual/with friends/colleagues, small running groups/clubs).
2.3. Measurements
2.3.1. Creating Scales of Running AIOs
First, we created scales of the items on running AIOs by replicating the questionnaire used by
Janssen et al. [41]. We ran reliability analyses for all scales. Items were assessed (Cronbach’s Alpha’s
scores of >0.700 were considered acceptable) and reconsidered whether they substantively contributed
to the component or not, and no changes were made. Finally, scales were constructed by calculating
the average scores for the reliable items per component, resulting in average scale scores. Table 1 gives
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an overview of these components (i.e., scales), including the number of items, Cronbach’s Alpha’s and
average score (ranging from 1 to 5). Eventually, ﬁve AIO-scales were formed and used in this study:
(1)
Perceived advantages of running (e.g., ‘running gives me energy’, or ‘running is good for
my health’);
(2)
Identiﬁcation with running (e.g., ‘I am proud to be a runner’, or ‘I feel myself to be a real runner’);
(3)
Running is a sport that is easy to practice (e.g., ‘I can practice running anytime, anywhere’);
(4)
Social motives for quitting (e.g., I would quit running ‘if my trainer quit’ or ‘if my running
friends quit’);
(5)
Individual motives for quitting (e.g., I would quit running if ‘I got injured’, or if ‘my spare time
was decreased’).
Table 1. Components including the number of items, Cronbach α, average scores and standard deviations.
Scale
Attitudes toward Running
Items
Cronbach α
N
Mean
SD
1
Perceived advantages of running
4
0.794
853
4.29
0.458
2
Identiﬁcation with running
5
0.738
853
3.33
0.640
3
Running as a sport that is easy to practice
3
0.781
853
4.22
0.623
4
Social motives for quitting
3
0.941
853
1.79
0.722
5
Individual motives for quitting
4
0.712
853
3.33
0.784
2.3.2. Dependent Variables
In this study, we used two dependent variables: social motives for quitting and individual motives for
quitting. As they do not follow a normal distribution, both scales were recoded into binary variables.
All scores below the scale average (i.e., M = 1.79) were coded as ‘0 below’ and all scores above the
average were coded as ‘1 above’. In this way, we were able to interpret the data relative to the sample
and able to see if there are variables that could explain why runners score lower or higher compared to
their fellow runners.
2.3.3. Independent Variables
As independent variables, we included three groups of variables: (i) socio-demographic variables;
(ii) running habits; and (iii) running AIOs. The socio-demographic characteristics included gender,
age, and level of education. The group of running habits consisted of variables that are directly related
to running and which deﬁne the level of running involvement: years of running experience, training
frequency and running context. The three-remaining scale on running AIOs perceived advantages of
running, identiﬁcation with running and running as a sport that is easy to practice complete the list of
independent variables. Table 2 gives the descriptive statistics of the sample for the dependent and
independent variables.
2.4. Analysis
All results were analyzed using SPSS 26.0 (IBM Corp., Armonk, NY, USA). First, descriptive
statistics (i.e., mean scores, standard deviations, minimum and maximum values) were collected to
provide an overview of the sample structure, and the items and variables used. Second, two binary
logistic regression models (method = enter) were created with the two dependent variables: social
motives for quitting and individual motives for quitting. As aforementioned, both scales were recoded into
binary variables. Nagelkerke R2 was used as a measure of goodness of ﬁt. Values between 0.10 and 0.20
were considered as satisfactory and above 0.20 as very satisfactory [44,45]. The diﬀerent models were
tested for multicollinearity, outliers, and leverage points by calculating the variance inﬂation factors
and inﬂuence statistics (Cook’s). No problems with the data were found concerning these aspects.
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Table 2. The descriptive statistics of the sample for the dependent and independent variables.
Variable
Measurement
n
%
Individual Motives Binary
Below
399
46.8
Above
454
53.2
Social Motives Binary
Below
390
45.7
Above
463
54.3
Gender
Male
387
47.8
Female
422
52.2
Age
≤35 year
261
32.1
36–45 year
239
29.4
≥46 year
313
38.5
Education
Lower or middle education
273
33.5
Higher education
332
40.8
University
209
25.7
Experience
<1 years
248
29.2
1–5 years
364
42.8
>5 years
238
28.0
Running frequency
≤1x/week
384
45.1
2x/week
350
41.1
≥3x/week
117
13.7
Running context
Individual
526
61.8
Friends, colleagues, small groups
226
26.6
Clubs
99
11.6
3. Results
3.1. Descriptive Analysis
First, descriptive analysis shows that the social motives for quitting scores an average of 1.79
(SD = 0.72) on a 5-point Likert scale. From the 853 runners, 390 (45.7%) runners score below the group
average, and the remaining 54.3% scores above and perceive relatively more social reasons to quit
running. For the individual motives for quitting a mean of 3.33 (SD = 0.78) on a 5-point Likert scale
was given. Here, of the 853 runners, 399 (46.8%) runners scored below this relative average, and the
remaining 46.8% perceived relatively more individual reasons to quit running. In Table 3, the mean
scores on the items that form both scales are presented. If we compare these items, it is clear to see that
‘physical constraints or injuries’ are the most important reason to quit running (M = 4.14 SD = 0.77),
followed by item 6; ‘tired of running’ (M = 3.20; SD = 1.05). The items that are related to ‘social motives
to quit running’, score the lowest (M = 1.82 or lower).
Table 3. Mean scores, standard deviations, minimum and maximum values of the items.
Item No.
Item
Mean
SD
Min
Max
1
My running partners quit running 1
1.82
0.85
1
5
2
My running group falls apart 1
1.80
0.84
1
5
3
My trainer/coach is leaving 1
1.76
0.80
1
5
4
Preference for another sport 2
3.06
1.04
1
5
5
Reduction of leisure time 2
2.95
1.05
1
5
6
Tired of running 2
3.20
1.06
1
5
7
Physical constraints or injuries 2
4.14
0.77
1
5
Superscript number indicate to which scale, the items belong to. Social reasons to quit running indicated with a 1,
and individual reasons indicated with 2.
Second, the results of the binary logistic regression are presented in Table 4. The binary logistic
regression with social motives for quitting running as a dependent variable showed signiﬁcant
Int. J. Environ. Res. Public Health 2020, 17, 6046
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diﬀerences (p < 0.05, p < 0.01 or p < 0.001) for gender, experience with running, running context and
on the AIOs towards running, viz. running as a sport that is easy to practice, perceived advantage
of running and identiﬁcation with running. The binary logistic regression with individual motives
for quitting running as a dependent variable revealed signiﬁcant diﬀerences for age, education level,
experience with running, running frequency and one of the AIOs towards running, viz. identiﬁcation
with running.
Table 4. Results of the binary logistic regression, in odds ratios (Exp (β)) with regards to the reference
group (ref.).
Social Reasons (n = 803)
Individual Reasons (n = 803)
Constant
646,050 ***
42,827 ***
Gender
Male
Ref.
Ref.
Female
1.642 **
1.234
Age
≤35 year
Ref.
Ref.
36–45 year
1.018
0.777
≥46 year
1.402
0.498 ***
Education
Lower or middle education
Ref.
Ref. ***
Higher education
1.193
2.012 ***
University
0.972
2.721 ***
Experience
<1 years
Ref.
Ref.
1–5 years
0.829
0.888
>5 years
0.610 *
0.610 *
Running frequency
≤1x/week
Ref.
Ref.
2x/week
0.717
0.654 *
≥3x/week
0.734
0.799
Running context
Individual
Ref. ***
Ref.
Friends, colleagues, small groups
3.352 ***
1.203
Clubs
4.541 ***
1.361
AIO toward running
Running as a sport that is easy to practice
0.502 ***
0.985
Perceived advantages of running
0.314 ***
0.992
Identiﬁcation
1.366 *
0.352 ***
Nagelkerke R2
0.278
0.244
* = p < 0.05; ** = p < 0.01; *** = p < 0.001.
3.2. Binary Logistic Regression Social Reasons for Quitting
In the model for ‘social motives for quitting running’, female runners were more likely (OR = 1.642;
p < 0.01) to perceive social motives to quit running than male runners. No eﬀect was found for age and
education. With regards to the running habits, runners with more than 5 years of running experience,
were less likely (OR = 0.610; p < 0.05) to perceive social motives to quit running compared to runners
with less than 1 year of running experience. Thereby, runners who run with other runners are more
likely to perceive social motives to quit running. Those who run with friends, colleagues and in
small groups have an odds ratio of 3.352 (p < 0.001) and those who run in clubs have an odds ratio
of 4.541 (p < 0.01), both compared to runners that participate individually. The third running habit;
running frequency did not show signiﬁcant diﬀerences. In the ﬁnal set of independent variables,
signiﬁcant diﬀerences for all included AIOs towards running were found. Those who see running as a
sport that is easy to practice (OR = 0.502; p < 0.01) and those who perceive advantages of running
(OR = 0.314; p < 0.01) were less likely to perceive social motives to quit running, whereas runners who
identify themselves with running (OR = 1.366; p < 0.05) were more likely to perceive social motives to
quit running.
3.3. Binary Logistic Regression Individual Reasons for Quitting
In the model for individual motives for quitting running, gender was not found to be associated
with the individual motives, were the other socio-demographic variables was. Runners that were older
(>46 years) are less likely to perceive individual motives to quit running than younger runners (<35
years) did (OR = 0.498; p < 0.001). Runners with higher education or who ﬁnished university, were
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more likely to quit running based on individual motives compared to runners with a lower of middle
education (resp. OR = 2.012; p < 0.001 and OR = 2.721; p < 0.001). Similarly, to the model on social
motives for quitting, runners with more than 5 years of running experience, were less likely (OR = 0.610;
p < 0.05) to perceive individual motives to quit running compared to runners with less than 1 year of
running experience. The running frequency was also found to be signiﬁcant, those who run twice a
week (OR = 0.654; p < 0.05) were less likely to perceive individual motives for quitting compared to
runners who run once (or less) a week. Furthermore, runners who identify themselves with running
(OR = 0.352; p < 0.001) were less to perceive individual motives to quit running. No signiﬁcant
diﬀerences were found for running context, and AIO-items running as a sport that is easy to practice
and perceived advantages of running.
4. Discussion
The aim of this study was to gain insight among short-distance event runners into the perceived
reasons to quit running, and to identify how these reasons are aﬀected by socio-demographics (i.e.,
gender and age), running habits and AIOs on running. This is an important step toward expanding the
evidence base to understand the reasons for dropout as perceived by runners. This is key to support
runners in continued running and to address the barriers runners perceive adequately. The limitations
of this study, such as the treatment of the data and its implications, are discussed at the end of the
discussion section.
Our ﬁndings show that runners are more likely to perceive individual reasons to quit running
than social reasons (Table 3). Physical constraints or injuries (item 7) is the most important reason to
quit running, which is in line with previous studies [29–33], followed by being tired of running (item 6).
Socials reasons to quit running because ‘my trainer is leaving’, or ‘my buddy quit running’ were less
likely to be perceived as important. A possible explanation for this might be that a large group of the
participants (approx. 60%) does not run in a social context but runs individually. This is in line with
studies showing that running is an activity that is mostly practiced individually, outside the organized
context of clubs [4,28,46]. For individual runners, individual reasons to quit running might be more
applicable and easier to identify with, as compared to social reasons.
For individual reasons to quit running, signiﬁcant diﬀerences were found for age, education level,
experience with running, running frequency and one of the AIOs towards running; identiﬁcation with
running (Table 4). Furthermore, results showed that social reasons to quit running are signiﬁcantly
diﬀerent depending on the gender, experience with running, running context and on the AIOs towards
running; running as a sport that is easy to practice, perceived advantage of running and identiﬁcation
with running.
Compared to male runners, our results show that female runners perceive more social reasons to
quit running. This result may be explained by the fact that women appear to attach greater value to
social support [47–49]. A previous study by Vos et al. [19], in which a typology of female runners was
constructed, did show that women valued connectedness with others. This ﬁnding was also reported
by Pridgeon and Grogan [49], stating that loss of social support contributed to exercise dropout,
especially among women. Another possible assumption would be that female runners, compared to
male runners, run more often in a social context and therefore experience social reasons to quit running
more often. However, this explanation is not supported by a previous study (N = 3727) on running
typologies, which does not suggest that women are more likely to run in social contexts but often run in
individual context as well [41]. Notably, in the present study, we did not found signiﬁcant diﬀerences
for individual reasons to quit running for gender. So, although female runners run in both social and
individual contexts, social reasons to quit running are perceived more often by women than men.
Runners aged above 45 years, perceive fewer individual reasons to quit running as compared to
younger runners below 35 years. This result might hint at the idea of people feeling more in control
of their own time when ageing, as compared to having diﬃculties in seeking a way to incorporate
running in their daily lives [47,48,50]. This might also be related to the fact that people over 45 are in a
Int. J. Environ. Res. Public Health 2020, 17, 6046
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less exploratory phase of their lives, and thus do not perceive reasons to seek for diﬀerent types of
sports to practice [50]. Another explanation might be that these ‘older’ runners are more experienced
and therefore, more aware of their bodies and potential injuries [30,51]. This is in line with a previous
study, indicating that the most experienced runners included most runners being older than 45 [41].
What is notable is that there is no signiﬁcant diﬀerence found for social reasons to quit running for age,
indicating that reasons to quit from a social perspective are not dependent on age.
Our results suggest that runners who have a higher education or university degree perceive more
individual reasons to quit running compared to runners with a low or middle educational degree.
Runners with a university degree perceive these reasons about three times as much, and runners with
a higher education twice as much. This is not the case for social reasons to quit running. The reason
for this might be that runners with a higher or university degree have more trouble in ﬁnding a good
work-life-sports balance, and thus have more trouble in prioritizing running on a day to day basis.
Running experience inﬂuenced both social and individual reasons to quit negatively, where
runners who run for more than 5 years perceive less (social and individual) reasons to quit as compared
to runners running for less than a year. We can hypothesize that runners who already have been
running for more than 5 years have already been able to overcome obstacles and barriers (e.g., injuries
or motivational loss) throughout the years and kept pursuing running [30,51]. On the other hand,
participants running for less than a year might have a lower self-eﬃcacy, i.e., conﬁdence in one’s
ability to overcome potential obstacles [52]. Another possible explanation is that experienced runners
might feel more competent, and therefore are less afraid of getting injured or being dependent on
external factors like a coach or a running group. A previous study, for instance, indicated that the more
experienced runners (>7 years) were more likely to run “for the love of running” [25], which might
indicate that regardless of some obstacles, their love for running helps them overcome these.
When looking at running frequency, the results suggest that runners who run twice a week
perceive fewer individual reasons to quit running as compared to runners who run once a week or less.
Notably, this is not the case for social reasons to quit running, nor for runners who run three times a
week or more. Although these runners who run twice a week have a higher time investment compared
to runners who run once a week or less, they might be able to better incorporate this activity in their
schedule on a weekly basis [39]. For those running ≤1 per week, the involvement into running is lower,
as compared to runners who dedicate to run twice a week. These ‘occasional’ runners might perceive
more reasons to quit since they have not been able to commit to the sport that often on a training basis
yet [39,41]. Additionally, a lower running frequency might also aﬀect the feeling of competence or
experience, which in turn might increase the fear of getting injured [30].
Although runners in our sample generally experienced more individual reasons to quit running,
the running context positively inﬂuenced social reasons to quit running. Runners who run in a running
group perceive more than three times as many social reasons to quit running compared to runners who
run individual, and runners running at a running club more than four times as much. It might seem
obvious that when one runs individually, fewer social reasons to quit can be observed. Interestingly
though, individual runners do not perceive more individual reasons to quit running, as compared
to social runners. Individual reasons to quit running might thus not be dependent on the running
context but on other variables (e.g., age, running experience, running frequency) as stated in earlier
studies [17,36,37].
Runners who do not think of running as a sport that is easy to practice, and do not perceive many
advantages of running, perceive more social reasons to quit running. Instead of these advantages
of running, these runners might value and need other AIOs (e.g., social support) to go running and
therefore, experience more social reasons to quit running [49].
When one identiﬁes as being a runner, our results indicate that this aﬀects both social and
individual reasons to quit running. Runners who identify themselves as a runner perceive more social
reasons to quit running. This might indicate that runners who run in a social context (e.g., club or
running group), identify themselves as being a ‘real’ runner and therefore might also depend more
Int. J. Environ. Res. Public Health 2020, 17, 6046
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on their fellow runners (as a community) and social support. When for example a fellow runner
quits, this might act as a trigger to quit running [49]. Contrary to this, runners who identify as
being a runner perceive less individual reasons to quit running. A possible explanation might be
that these are less likely to get tired of running, or running is their main sport. This is in line with
previous studies indicating that runners who identify strongly with running are the more experienced,
long-distance runners [41,43], hinting they might have been able to overcome these possible reasons to
quit previously.
Based on our results, we argue that although we see signiﬁcant diﬀerences related to gender
in social reasons to quit running and signiﬁcant ones related to age in individual reasons to quit
running, these should not be considered conclusive. Our results showed that running characteristics
(e.g., running experience, context, frequency, running AIOs) also inﬂuence one’s perceived reasons
to quit running. We thus contribute to knowledge on running dropouts by drawing a more accurate
picture of the situation.
Limitations
Our study has some limitations. As part of our sampling strategy, we selected a subset of the
dataset and included runners who participated in the 5 and 10 km distances of the running event.
Through this, we purposively focused on novice and less experienced runners, who are more likely to
drop-out. Although these runners might not be representative of all runners who perceive reasons to
quit running, participants of large running events have been considered a representative selection of
the broader recreational running community in previous studies [41,53].
In this study, we investigated runners’ perceived reasons to quit running. By asking perceived
reasons, this study relies on self-reported data and the perception of the participants. We do not
know if these reasons would be an actual reason to quit running. However, knowing more about the
perception of runners might indicate possible solutions or interventions to lower drop-out rates.
Finally, some methodological limitations related to the dependent variables should be mentioned.
As aforementioned, we had to recode our two dependent variables into binary variables because both
scales were not normally distributed. We thus lost some information about individual diﬀerences.
Yet, we were able to interpret the data relativity to the sample. Second, we used 7 items to construct the
2 independent variables. Next to these seven possible reasons to quit running, there are other reasons
why runners may quit running. Here we decided to build further on previous studies and hence could
beneﬁt from items which have an acceptable internal consistency.
5. Conclusions
Our survey study shows that although gender and age have shown signiﬁcant diﬀerences in
perceived reasons to quit running, these should not be considered conclusive. Our ﬁndings implicate
that running characteristics (e.g., running experience, context, frequency, running AIOs) also inﬂuence
one’s perceived reasons to quit running. These insights could help policymakers to understand novice
runners and their perceived reasons for a potential drop-out. This insight can be used to match public
health policies to the motives and barriers of novice runners. Sports professionals (e.g., trainers and,
coaches) could use this insight to lower drop-out rates among novice runners and eliminate potential
perceived reasons to quit running.
Supplementary Materials: The following are available online at http://www.mdpi.com/1660-4601/17/17/6046/s1,
File S1: Figure of Descriptive Statistics and File S2: Mean scores and SD of the Items.
Author Contributions: Conceptualization, D.M., M.J. and S.V.; methodology, M.J. and S.V.; formal analysis, M.J.
and S.V., investigation, M.J. and S.V.; writing—original draft preparation, D.M., M.J.; writing—review and editing,
D.M., M.J., M.F., E.v.d.S., C.L. and S.V. All authors have read and agreed to the published version of the manuscript.
Funding: This work is part of the project Nano4Sports which is ﬁnanced by Europees Fonds voor Regionale
Ontwikkeling Interreg Vlaanderen Nederland award number(s): 0271.
Int. J. Environ. Res. Public Health 2020, 17, 6046
10 of 12
Acknowledgments: We would like to thank the organization and the runners of the Marathon Eindhoven 2016,
for their help and time to take part in our online survey. Without their involvement, this study would not have
been possible.
Conﬂicts of Interest: The authors declare no conﬂict of interest.
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Runner's Perceptions of Reasons to Quit Running: Influence of Gender, Age and Running-Related Characteristics.
Menheere, Daphne,Janssen, Mark,Funk, Mathias,van der Spek, Erik,Lallemand, Carine,Vos, Steven