1
Content Discussion
Student’s Name
Department, University
Course Name: Course Code
Professor’s Name
Submission Date
2
Content Discussion
Value Proposition Article
The value proposition is the value indicated by an organization that defines how the firm’s
benefits will be disseminated and acquired by the customers. It elaborates to the customers about
the specific characteristics of the products and services that make them exemplary and different
from the rest. It urges the customers why they should purchase particular a product. I learned that
value proposition is entirely concerned about the customers. The focus on the customers stood
out in the article. The article made me feel that customer evaluation should be the first action that
firms should undertake to achieve their vision and goals.
Inc Africa. Build A Product People Will Buy
The article discusses the importance of choosing a product that people will buy in the
competitive market. It illustrates by giving a real-life example of Warnke and Fossen, who came
up with Surreal Wi-Fi and Backup Box company (Zetlin, 2012). Warnke explained that Surreal
Wi-Fi focused on the developers and not the customers. I learned that it is significant to consider
the customers when creating a product and not yourself. The article made me change my plans of
developing an online business in our institution. I realized that not every student has access to
online devices such as laptops and mobile phones. I agree with the content of the article.
3
Business Model Innovation. What It is and why it is Important
The article elaborates the concept of the business model and its importance to organizations
across the globe. The report indicates that a business model is a strategy enacted by the
organization to enhance the value of its products, thus meeting the customer’s expectations
(Landry, 2020). Business model innovation is the incorporation of processes that adjusts the
business model to suit the dynamic customer needs. For instance, the video game industry has
been enacting various changes to boost its customer base. I agree with the greater part of the
article but would have liked to know how an entrepreneur can incorporate an innovation that is
different from his line of operations. For instance, if an entrepreneur deals with the supply of
milk, how can he incorporate digital processes with the growing digitalization of the economy.
Minimum Viable Product and Pivoting: Crash Course Business Entrepreneurship #6
The minimum viable product and pivoting illustrate about the strategies entrepreneurs need to
take when introducing a new product in the market. The entrepreneur should be observant of
what to offer the customers because there is a possibility of offering products that won’t sell in
the market. For instance, when YouTube was launched, it was a dating site where people hook
up, but with time, the developers discovered that the idea did not conform with the market needs
(Crash Course, 2019). The video made me learn that it is normal to start small in a business. I
comprehended the mantra dream big, start small and will be applying it in my forthcoming
projects. I agree that an entrepreneur should not seek perfection immediately he introduces a
product in the market. It is true that market forces affect revenue generation during the first
phases of a product in the market. The video made me feel that a small start can turn out to be an
essential step if the entrepreneur conforms with the minimum viable and pivoting process.
4
References
Landry, L. (2020). Business Model Innovation. What It is and Why it is important.

Crash Course, (2019). Minimum Viable Product and Pivoting: Crash Course Business
Entrepreneurship.https://m.youtube.com/watch?v=m2IPvT920XM&list=PL8dPuuaLjXtNam
NKW5qlS-nKgA0on7Qze&index=5
Zetlin, M. (2012) Build a Product Customers Will Buy: 5 Tips. https://www.inc.com/mindazetlin/how-to-build-product-customers-will-buy.html
Corporate Finance Institute. Value Proposition.
https://corporatefinanceinstitute.com/resources/knowledge/strategy/value-proposition/
Journal of Physical Education and Sport ® (JPES),Vol 21 (Suppl. issue 3), Art 249 pp 1958 – 1966, July.2021
online ISSN: 2247 – 806X; p-ISSN: 2247 – 8051; ISSN – L = 2247 – 8051 © JPES
Original Article
Mental health in sports students – a cohort study on study-related stress, general
well-being, and general risk for depression
CAROLIN BASTEMEYER1, JENS KLEINERT2
1,2
Institute of Psychology, Department Health and Social Psychology, German Sport University Cologne,
GERMANY
Published online: July 31, 2021
(Accepted for publication July 15, 2021)
DOI:10.7752/jpes.2021.s3249
Abstract
Previous research on mental health in sports students often focused on individual aspects (e.g., stress, well-being,
and risk for depression) instead of looking at mental health in its entirety. Therefore, the main objective of this
study is to offer a more comprehensive analysis of mental health by focusing on the specific target group of
sports students and examining study-related stress and its relationships to general well-being and general risk for
depression at different points of time during their studies. The sample consisted of 648 sports students (413 male,
234 female, 1 other). The students belonged to one of three cohorts of sports students in Germany (first-year BA
students; BA students in their final stages; MA students). Mental health was measured with study-related stress,
well-being, and risk for depression. Overall and depending on aspects of mental health, between 5% and 20% of
sports students showed rather poor mental health. More than 50% sometimes or rather frequently exhibited
study-related stress. 21% of sports students showed impaired well-being levels and 11% had an increased risk for
depression. Differences occurred between cohorts (i.e., more advanced sports students had poorer mental health).
Moreover, the results of the regression analysis revealed that study-related stress is a predictor of well-being and
risk for depression in sports students. The longer sports students studied, the lower was their experienced mental
health. Possible explanations could be increasing study-related requirements or upcoming graduation (i.e.,
exams, fear of the future). Future research should examine causes of impaired mental health in sports students.
Keywords: psychological health, university students, athletes, higher education, study load
Introduction
Studies on mental health in students deal with various aspects, especially with stress, well-being, and
risk for depression. Of these aspects, stress receives particular research attention. In terms of stress levels,
university students experience high amounts of stress during their study programs (Bayram & Bilgel, 2008).
These stress levels can have negative effects on students’ academic performance (Bennett, 2007; Janse van
Rensburg et al., 2011) but are also often associated with mental disorders (e.g., depression). Recent studies
indicate prevalence rates for mental disorders in students between 20% and 30% (Bailer et al., 2007; Grobe et
al., 2018; Gusy et al., 2016; Meier et al., 2010).
Within the group of students, sports students (i.e., sport science students as well as students of physical
education (PE students)) represent a special target group when considering stress at university (i.e., study-related
stress). They are confronted with study-related stressors that are not only caused by typical academic demands
(i.e., examinations) but also by physical and sporting demands (i.e., physical performance, training) (Brown et
al., 2015; Proctor & Boan-Lenzo, 2010; Wilson & Pritchard, 2005). This combination of academic and physical
demands may have a special impact on study-related stress processes in sports students. Whether study-related
stress affects the overall well-being of students and their general risk for depression is largely unknown.
Therefore, the main objective of this study is to focus on the specific target group of sports students and to
examine study-related stress and its relationships to general well-being and risk for depression at different points
of time during their studies. The results of this study should be discussed with the perspective of developing
specific health prevention programs for sports students.
Stress in sports students
According to the transactional stress model, stress arises when people perceive that they cannot
adequately cope with given demands or with threats to their mental health (Lazarus, 1984). Stress is caused by
personal appraisals in a process of transaction between person and environment (Lazarus & Folkmann, 1984).
Regarding the stress of students, this means that a student rates the significance of a situation for their own
mental health, e.g., the significance of an examination.
So far, only few studies have examined stress in sports students (Bastug et al., 2014; Demirel, 2016;
Eraslan & Dunn, 2015; Kumar & Bhukar, 2013; Schäfer et al., 2019; Verma et al. 2011). The stress levels of
sports students differ compared to students of other subjects (Demirel, 2016); furthermore, the stress levels of
physical education teachers differ regarding their career phases (Schäfer et al., 2019). Demirel (2016) compared
1958——————————————————————————————————————————–Corresponding Author CAROLIN BASTEMEYER, E-mail: [email protected]
CAROLIN BASTEMEYER, JENS KLEINERT
——————————————————————————————————————————–stress levels of sport science students and university students of other subjects. The results showed that sport
science students had statistically significantly higher stress levels than non-athlete students. As possible
explanations, Demirel (2016) mentioned special stressors for sports students, such as the fear of forthcoming or
physical stress associated with their training program. Schäfer et al. (2019) examined perceived stress levels of
physical education teachers in different career stages (PE students, pre-service teachers, and PE teachers). PE
students perceived lower stress levels compared to pre-service teachers and PE teachers.
Further studies found that stress levels of sports students differed in terms of gender (Bastug et al., 2014; Eraslan
& Dunn, 2015; Kumar & Bukhar, 2013; Verma et al., 2011) and age (Eraslan & Dunn, 2015): Perceived stress
levels of female students were significantly higher than those of male students (Bastug et al., 2014; Kumar &
Bukhar, 2013; Verma et al., 2011,). In contrast to the aforementioned findings, Eraslan & Dunn (2015) indicated
that the stress levels of the research sample does not differ significantly in terms of gender and age.
Well-being in sports students
There is no universal definition of well-being. There are rather many descriptions for this construct
(Dodge et al., 2012). Psychological well-being is a central part of individual health (WHO, 1946) and shows the
feelings of a person that range from negative mental conditions (e.g., depression) to positive mental conditions
(e.g., life satisfaction) (Bradburn, 1969; WHO, 1946).
So far, well-being in sports students has been investigated in a few studies (Gönener et al., 2017;
Güngör & Çelik, 2020; Juriana et al., 2019). Juriana et al. (2019) highlighted that most of the analyzed sports
students had a moderate psychological well-being (73.5%). 13.1% of their sample belonged to the high category
(good/high well-being) and 13.4% of the examined sports students belonged to the low category (bad/low wellbeing) of psychological well-being. Güngör & Çelik (2020) examined the mental well-being of sports students
and revealed that the mental well-being levels of their study participants were above the average (obtained from
the Warwick-Edinburgh Mental Well-being Scale). There were no significant gender or class differences. The
results of Gönener et al. (2017) showed that male sports students had significantly higher (better) scores in
mental well-being than female sports students. As in Güngör’s & Çelik´s study (2020), no significant differences
were found between class variables.
Depression in sports students
The American Psychiatric Association defines depression (major depressive disorder) as “a common
and serious medical illness that negatively affects how you feel, the way you think and how you act.” (American
Psychiatric Association, 2018). Feelings of sadness and/ or a loss of interest in activities a person once enjoyed
are symptoms of depression.
To date, depression in sports students has been investigated in two studies (Boath et al., 2013; Demirel, 2016).
Demirel (2016) showed that sports students had higher depression scores than students of other subjects. In
contrast, Boath et al. (2013) found no differences between the depression scores of sports students and students
of other subjects.
Research questions of the present study
In summary, there is a lack of consistent research on sports students’ mental health. Most of the studies
on mental health in sports students focused on stress especially. Only a few studies examined well-being and risk
for depression. Additionally, it must be noted that the mentioned studies differ in many aspects (e.g., sample of
sports students, measurements to assess the dependent variables, study design). Therefore, it is difficult to
compare the results of the mentioned studies or to rate the mental health of sports students. Additionally, it is of
interest to find out whether there are relationships between the three parameters study-related stress levels (i.e.,
stress in the context of studies and exams), levels of general well-being, and general risk for depression (i.e.,
stress and depression in overall life). Previous research provides evidence that these parameters are at least
moderately correlated with each other (Beiter et al., 2015; Dalton & Hammen, 2018; Eganov et al., 2020;
Lebensohn et al., 2013; Yüksel & Bahadir-Yilmaz, 2019; Zhang et al., 2018). Here, it is relevant to find out
whether and to what extent study-related stress statistically predicts general well-being and risk for depression.
Therefore, the following research questions were developed:
1) How high are study-related stress levels, levels of general well-being, and general risk for depression
among sports students?
2) Are there differences in study-related stress levels, levels of general well-being, and general risk for
depression between different semester cohorts and different study programs (i.e., BA, MA)?
3) Is there a correlation between study-related stress, general well-being, and general risk for depression?
4) Is study-related stress of sports students a statistical predictor of general well-being and general risk for
depression?
Material & methods
Participants
The sample consisted of 648 sports students (36.1% female; age: M = 22.61 years, SD = 3.28 years).
The students belonged to one of three cohorts of sports students. The 1st cohort consisted of first-year students in
the bachelor program (BA; n = 286, 34.9% female; age: M = 20.61 years, SD = 2.65 years; semester: M = 1.09,
SD = 0.4). The 2nd cohort consisted of sports students in the final stages of their BA program (n = 209, 33.4%
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CAROLIN BASTEMEYER, JENS KLEINERT
——————————————————————————————————————————–female; age: M = 22.9 years, SD = 2.23 years; semester: M = 5.43, SD = 0.95). The 3rd cohort consisted of sports
students in the middle of their master program (MA; n = 163, 42.9% female; age: M = 25.88 years, SD = 2.64
years; all students were in the 3rd semester).
Measures
Study-related stress. Study-related stress was measured using the German version (Fliege et al., 2001)
of the Perceived Stress Questionnaire (PSQ; Levenstein et al., 1993). The PSQ is an instrument to assess
experienced stress (independent of a specific or objective event). The PSQ consists of 20 items in four scales
with five items on each scale: “worries“ (e.g., “You are afraid of the future.”), “tension” (e.g., “You have trouble
relaxing.”), “joy” (e.g., “You feel you’re doing things you really like.”), and “demands” (e.g., “You feel that too
many demands are being made on you.“). Three scales assess internal stress reactions (worries, tension, and joy)
and the scale “demands” assesses the perception of external stressors. In order to measure study-related stress,
the PSQ instruction asked the students to indicate how often a statement of the questionnaire applied to them
during the last four weeks of their studies (“How do you feel about your studies? Among the four answers,
please mark the one that indicates how often the statement applies to you during the last four weeks.”). The PSQ
has a 4-point scale (1: “almost never,” 2: “sometimes,” 3: “often,” 4: “usually”). Mean values were formed for
each scale. For an overall study-related stress score, the mean values of all items were computed. For this study,
values above 3 represented a high study-related stress level. The PSQ is an economic, reliable (overall score: α =
.75; worries: α = .80; tension: α = .81; joy: α = .75; demands: α = .35), structurally stable, and valid instrument
that enables assessing perceived stress in different subject groups.
General well-being. Well-being was measured using the German version (Bonsignore et al., 2001) of
the WHO-5 Well-Being Index (WHO-5; WHO, 1998). The WHO-5 is a screening tool that assesses
psychological well-being during the past two weeks with five items (e.g., “Over the past two weeks…I have felt
calm and relaxed.”) on a 6-point scale from 0 (“at no time”) to 5 (“all of the time”). For an overall score, mean
values of each item were calculated. High values of the WHO-5 represent a good well-being. The WHO-5 is an
economic, reliable (α = .84), and valid instrument to assess psychological well-being (Bonsignore et al., 2001).
With a total score of 0 – 25, the WHO (1998) proposed a cut-off score of ≤ 12 to screen depression. In their
systematic review, Topp et al. (2015) reported a mean sensitivity of 86% and a mean specificity of 81% for
detecting a major depression in adults.
General risk for depression. Risk for depression was measured using the German version (Löwe et al.,
2005) of the Patient Health Questionnaire (PHQ-2; Kroenke et al., 2003). The PHQ-2 is a screening tool to
assess the risk for depression over the past two weeks with two items (instruction: “Over the past two weeks,
how often have you been bothered by any of the following problems?”; items: “Little interest or pleasure in
doing things.” and “Feeling down, depressed, and hopeless.”; α = .65) on a 4-point scale from 0 (“not at all”) to 3
(“nearly every day”). High values of the PHQ-2 represent a high risk for depression. The PHQ-2 is a valid
screening instrument to assess the risk for depression (Arroll et al., 2010). With a total score of six on a scale of
0 – 6, Löwe et al. (2005) found a sensitivity of 87% and a specificity of 78% for major depressive disorders. For
any depressive disorders, the authors found a sensitivity of 79% and a specificity of 86%. Kroenke et al. (2003)
proposed a cut-off score of ≥ 3 to screen depression.
Procedure
After approval by the ethics committee, participants were recruited during seminars at the university.
For this purpose, the university teachers of the respective seminars were contacted by e-mail (via an internal
mailing list) and asked to let the survey take place within their courses. Once consent was obtained, the study
instructors went to the courses and distributed paper questionnaires to the students. The data collection took
place at three different points in time (November 2016 (1st cohort: first-year BA students; 1st/2nd semester),
January 2018 (2nd cohort: BA students in the final stages of their program; 5th/6th semester), and January 2019
(3rd cohort: MA students; 3rd semester)).
On the first page of the questionnaire, the participants were informed about the overall objectives of the study,
the voluntary nature of participation, and the anonymization of their data or person. All students completed the
questionnaires during their presence in seminars at the university. The completion of the questionnaires took
about 20 minutes.
Data analysis
Data were analyzed by using IBM SPSS Statistics 27. First, the data set was cleaned by using outlier
analysis. In order to examine study-related stress, well-being, and risk for depression, descriptive analyses were
run. Differences between the three cohorts in study-related stress levels, levels of well-being, and risk for
depression were calculated using multivariate analyses of variance (MANOVAs).
To investigate correlations between overall study-related stress levels, the four dimensions of stress, general
well-being, and general risk for depression, a Pearson correlation analysis was performed. Subsequently, a
multiple regression analysis (enter) was run to analyze whether and to what extent the four dimensions of studyrelated stress predict general well-being and general risk for depression.
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CAROLIN BASTEMEYER, JENS KLEINERT
——————————————————————————————————————————–Results
Levels of study-related stress, well-being, and risk for depression
Table 1 shows the descriptive data of study-related stress, well-being, and risk for depression. In terms
of overall study-related stress, 44% of all sports students (n = 645) reported low levels of study-related stress
(overall stress score, values lower than 2 “sometimes”). 50.3% reported moderate study-related stress levels
(overall stress score, values between 2 “sometimes” and 3 “often”). Only 5.7% of students surveyed reported
high study-related stress levels (overall stress score, values between 3 “often” and 4 “usually”).
Well-being was determined by calculating a cut-off score. Scores below or equal to 12 indicated an impaired
well-being (WHO, 1998). With a cut-off score of ≤ 12, 21.6% of all students surveyed (n = 645) showed an
impaired well-being.
The risk for depression was also determined using a cut-off score. Scores above or equal to 3 indicated a high
risk for depression (Kroenke & Spitzer, 2003). With a cut-off score of ≥ 3, 11.6% of sports students surveyed (n
= 646) had a high risk for depression.
Table 1. Descriptive data of study-related stress, well-being, and risk for depression
Differences in study-related stress levels, levels of well-being, and risk for depression between the three
cohorts
Overall study-related stress levels. In terms of overall study-related stress levels, the results of the
MANOVA (see Table 2) showed significant differences between the three cohorts (F (2, 641) = 18.84, p < .001,
η² = .06). While only 2.8% of the 1st cohort and 4.9% of the 2nd cohort reported experiencing stress “often” or
“usually,” this value was already 10.3% for the students of the 3rd cohort. Bonferroni-adjusted post hoc tests
revealed that MA students had higher overall stress levels than students of the 1st (p < .001) and 2nd (p = .004)
cohort. The 2nd cohort had significantly higher overall stress levels than the 1st cohort (p = .012).
Four dimensions of study-related stress levels. To highlight differences between the four dimensions of
stress within the three groups of students, a MANOVA was calculated. Results showed significant differences
between the cohorts for worries (F (2, 641) = 8.08, p < .001, η² = .03), demands (F (2, 614) = 11.01, p < .001, η²
= .03), joy (F (2, 641) = 21.38, p < .001, η² = .06), and tension (F (2, 641) = 13.6, p < .001, η² = .04) (see Table
2). Bonferroni-adjusted post hoc tests revealed that MA students (3rd cohort) had significantly higher worries (1st
cohort: p < .001), higher demands (1st cohort: p < .001, 2nd cohort: p = .002), and higher tension (1st cohort: p <
.001, 2nd cohort: p = .008) than BA students. The 1st cohort had significantly higher joy than the 2nd cohort (p <
.001) and 3rd cohort (p < .001). The 2nd cohort in turn had significantly higher joy than the 3rd cohort (p = .04).
Levels of well-being. Within the three cohorts, the results for impaired well-being differ significantly (F (2, 641)
= 21.28, p < .001, η² = .06; see Table 2). While 10.5% of the 1st cohort showed an impaired well-being, 27.7% of
the 2nd cohort and 33.8% of the 3rd cohort reported an impaired well-being. Bonferroni-adjusted post hoc tests
revealed that less advanced sports students (1st cohort) had significantly higher well-being levels than the 2nd (p <
.001) and 3rd cohort (p < .001). There were no significant differences between the 2nd and 3rd cohort.
Risk for depression. Within the three cohorts, the results for students reporting a high risk for
depression differed significantly (F (2, 641) = 18.48, p < .001; η² = .06; see Table 2). While 6% of the 1st cohort
had a high risk for depression, 11.7% of the 2nd cohort and 21.7% of the 3rd showed a high risk for depression.
Bonferroni-adjusted post hoc tests revealed that more advanced sports students (2nd & 3rd cohort) had a
significantly higher risk for depression than less advanced sports students (1st cohort: p < .001). There were no
significant differences between the 2nd and 3rd cohort.
Table 2. Results of the MANOVA of study-related stress, well-being, and risk for depression
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CAROLIN BASTEMEYER, JENS KLEINERT
——————————————————————————————————————————–MANOVA all DVs
Variable
Stress
Scale
Worries
Tension
Joy
Demands
Overall
Well-being
Risk for
depression
df1
2
2
2
2
2
df2
641
641
641
641
641
F
8.08
11.01
21.38
13.6
18.84
p
< .001
< .001
< .001
< .001
< .001
η²
.025
.033
.063
.041
.056
2
2
641
641
21.28
18.48
< .001
< .001
.062
.055
Relationship between study-related stress, well-being, and risk for depression
Table 3 shows bivariate correlations between overall study-related stress, the four stress dimensions (worries,
tension, joy, and demands), well-being, and risk for depression. Results showed that correlations between all
examined variables were significantly high.
Overall study-related stress showed a high positive correlation with well-being (r = .62, p < .001) and a high
negative correlation with risk for depression (r = -.70, p < .001). Results were similar for the four dimensions of
stress: Except for the positively oriented dimension joy, all subscales of stress had a positive correlation with
well-being and a negative correlation with risk for depression. Conversely, the dimension joy showed a positive
correlation with well-being (r = -.54, p < .001) and a negative correlation with risk for depression (r = .65, p <
.001). Except for the dimension demands, all results of the correlation analysis can be described as high
correlations (according to Cohen, 1988). Demands correlated moderately with well-being (r = .39, p < .001) and
risk for depression (r = -.58, p < .001).
Table 3. Bivariate correlation of study variables (i.e., study-related stress, well-being, and risk for depression)
Variables
(1) Overall stress
N
647
(2)
.84**
(3)
.89**
(4)
-.75**
(5)
.82**
(6)
.62**
(7)
-.70**
(2) Worries
647
–
.69**
-.55**
.57**
.56**
-.51**
(3) Tension
647
–
–
-.57**
.70**
.55**
-.67**
(4) Joy
647
–
–
–
-.39**
-.54**
.65**
(5) Demands
647
–
–
–
–
.39**
-.58**
(6) Well-being
646
–
–
–
–
–
-.65**
(7) Risk for depression
Note. ** p < .001, two-tailed
645
–
–
–
–
–
–
Study-related stress as a predictor impaired well-being and risk for depression
In order to examine whether study-related stress is a predictor of impaired well-being and risk for depression,
multiple regression analyses were conducted.
Results showed that study-related stress is a significant predictor of well-being (F (4, 644) = 198.51, p <
.001); the dimensions of stress predicted 55.1% of the variance of well-being. The analyzed regression model
revealed tension (β = -2.797, p < .001) and joy (β = 2.979, p < .001) as significant predictors of well-being (see
Table 4).
Study-related stress was a significant predictor of risk for depression (F (4, 645) = 114.33, p < .001).
The dimensions of stress predicted 41.3% of the variance of risk for depression. The analyzed regression model
revealed worries (β = .274, p < .001), tension (β = .261, p < .001), and joy (β = -.256, p < .001) as significant
predictors of risk for depression (see Table 4).
Table 4. Multiple regression analyses for well-being and risk for depression
Well-being (N = 644)
p
.887
R
β
-.01
Tension
-.41
-9.34

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