The Relationship Between Academic Stress and Skeletal Muscle Performance
Voon Chi Chia, Angie L. Wei, Nicole A. Laskosky, Courtney D. Jensen
Department of Health and Exercise Science, University of the Pacific, Stockton, CA
ABSTRACT
Table 1: Demographics
Student athletes are required to perform both in the classroom and on the field; balancing these
commitments can be stressful. It is common to question the burden of athletic demands on
student scholarship. However, the inverse is seldom asked: how do scholastic stresses affect
athletic performance? Many studies show the negative effects of anxiety on performance5; there
is also ample research on the effects of pre-game stress on athletic performance.3 However,
studies on the physiological effect of scholastic stress on athletic performance is sparse.
Characteristics
Female (N = 13)
Male (N = 10)
Age
20
20
Major
9
8
Course Credits
16.5
15.9
LE Injuries
5
6
BMI
23.0
24.2
Jobs
6
4
Job (Hours per Week)
2.5
2.7
Sport Participation
10*
1
Intramural Sports
0
7*
Club Sports
1
2
PURPOSE: To test the effect of psychological stress on skeletal muscle performance in
college students.
METHODS: We enrolled 23 recreationally active students (10 men, 13 women) from a D1
university. Skeletal muscle function was assessed via quadriceps extension and hamstring
flexion using a Cybex HUMAC NORM dynamometer. We define stress as the inequality
between perceived ability and the amount of demands2; psychological stress was measured
with the Cohen Perceived Stress Scale1. Subjects were evaluated at two time points: a high
stress period (exams) and a low stress period (no exams). Subjects with history of lower
extremity injuries were omitted from participation; nightly sleep, history of exercise, and
recent exercise were controlled. Independent variables were stress, sex, age, weight, BMI,
academic load, and participation in organized sports (club or intramural). Dependent
variables were peak torque (ft/lb) and time to achieve peak torque (sec). Differences in
muscle performance between high and low stress periods were assessed with t-tests. Linear
regressions analyzed the effect of psychological stress on muscle performance.
RESULTS: Subjects were 20.2 ± 1.1 years old, had peak flexor torque of 87.4 ± 19.7 ft/lb
(achieved in 0.58 ± 0.12 sec), and peak extensor torque of 145.2 ± 37.5 ft/lb (achieved in
0.58 ± 0.15 sec). T-tests found no differences between low and high stress periods in peak
torque or time to achieve peak torque (p>0.090). Linear regression found increases in
psychological stress correlate with improvements in the overall rate of force development
(p=0.004). The effect was most strongly observed in flexors: for each point that stress
increased, time to achieve peak torque was 2.4% faster (p=0.002).
CONCLUSION: Although our sample was small, our findings suggest that psychological
stress may enhance force development. A possible mechanism is stress-related elevations in
epinephrine levels. Although this was not measured, an increase in epinephrine could
potentiate calcium release to accelerate contraction. Academic stress likely presents many
challenges for student athletes, such as sleep deprivation6, but it might not impair muscular
performance.
EXPERIMENTAL DESIGN AND METHODS
The research population consisted of male (n=10) and female (n=13) undergraduate students of
various majors and class standings who were currently enrolled full time at a D1 university in
northern California.
Our inclusionary criteria were 1) full-time college enrollment, and 2) age between 18 and 22
years. The exclusionary criterion was lower extremity injuries that prohibited muscle testing or
extreme psychological distress, which may confound functioning. Ethical concerns were
addressed by de-identifying all subjects in the database.
Prior to muscle function testing on the Cybex HUMAC NORM dynamometer, we recorded
demographic, behavioral, and anthropometric data: height, weight, BMI, age, sex, major,
academic load, frequency and duration of exercise per week, amount of sleep per night, and
history of lower extremity injury. Occupations and university extracurricular sport engagement
(intramural and club) were also considered in the muscle test. Data collected using the Cybex
system included peak torque, time to peak torque, and duration peak torque was held. The
subjects performed this protocol at two time points during the semester to compare stress levels.
Psychological stress was assessed with the 10-Point Cohen Perceived Stress Scale
Questionnaire, which evaluated the degree of stress the individual was currently experiencing
and how they were responding to that stress. Subjects answered numerically on a scale of 1-4;
answers were summed to obtain a composite score.
Statistics. All tests were performed using SPSS version 22 (IBM SPSS Statistics, IBM
Corporation, Chicago, IL, USA). Differences between men and women were measured with chisquare and t-tests. Linear regression analyses tested the effect of psychological stress score on
skeletal muscle function. Significance was set at p < 0.050.
[Above] Cybex HUMAC NORM Dynamometer
RESULTS
* = p < 0.05. LE = Lower Extremity. BMI = Body Mass Index.
Table 2: Two-Week Period Evaluations
Female
Male
Low Stress
High Stress
Low Stress
High Stress
Exercise per Week
2.385
2.231
2.400
2.300
Duration (Hours)
2.308
2.615
2.600
2.800
Sleep (Hours)
3.231
3.000
3.077
2.600
Subjects. Among male and female participants, no significant differences were detected in
peak torque (p = 0.082) or amount of time to reach peak torque (p = 0.391). All respondents
took an average of 0.580 ± 0.120 seconds to reach peak torque. Between men and women, no
differences were detected in the duration that peak torque was held (p = 0.228) (Table 3).
Relationship between Psychological Stress and Muscle Performance. Regarding change in
psychological test values and muscle performance, no significant differences were found
between change in peak torque (p = 0.992) or the duration of peak torque was held (p = 0.972).
However, the relationship shows a change between overall psychological stress and rate of force
production (p = 0.026) using the multiple linear regression.
Table 3: Muscle Function Tests (Mean ± SD)
Female (N = 13)
Male (N = 10)
Sum Peak Torque Flexor and
Extensors
213.492 ± 26.609
257.457 ± 68.922
Overall Average Time to Peak Torque
0.599 ± 0.136
0.554 ± 0.0968
Overall Average CYBEX Duration of
Peak Torque
0.0595 ± 0.0109
Regression analysis. The Regression Residual Model (F = 9.392, p = 0.001) explained about
48% of the variance in the time to reach peak torque. According to the model, with each point
that the Overall Psych Score increased, peak torque was accomplished about a tenth of a second
faster (95% Confidence Interval: -0.014 to -0.003).
SUMMARY AND CONCLUSIONS
0.0533 ± 0.00513
While we found that mental stress did not significantly affect peak torque or the duration peak
torque was held, we found that stress did have an inverse relationship with the time it took to
achieve peak torque. Psychological stress experienced by university students and athletes may
not be as detrimental as we initially assumed. Our findings suggest that an increase in stress
could potentially accelerate force development. For athletes, an increase in stress may mean an
enhancement of performance as the rate of force generation is critical to optimal performance in
explosive sport contexts.
Table 4: Multiple Linear Regression
Model
R
R Square
1
0.696a
0.484
Model
Sum of Squares
Df
Mean
Square
F
Sig.
1 Regression Residual
Total
0.053
0.056
0.109
2
20
22
0.026
0.003
9.392
0.001b
Model
Unstandardized
Coefficients
Standardized
Coefficient
t
Sig.
B
1
Std.
Error
(Constant)
-0.200
0.015
Change in Overall
Psych Score
-0.009
0.003
Intramural or Club
Sport participation
0.074
0.023
95%
Confidence
Interval for B
Future research with larger samples and diverse populations would be needed to confirm our
findings. Additionally, the conditions of stress should be varied and more comprehensive. Our
chosen population, the number of enrolled subjects, the conditions of stress, and the duration of
our research protocol limit the generalizability of our findings.
Collinearity
Statistics
Beta
Lower Upper
Bound Bound
Tolerance
-1.291
0.211
-0.052
0.012
-0.524
-3.242
0.004
-0.014 -0.003
0.986
3.239
3.239
0.004
0.026
0.986
0.121
REFERENCES
1. Cohen, S., Kamarck, T., and Mermelstein, R. (1994) A global measure of perceived stress. Journal of
Health and Social Behavior, 24, 386-396.
2. Einberg, R.S., & Gould, D. (2015) Foundations of sport and exercise psychology (5th Ed.). Champaign,
IL: Human Kinetics.
3. Fullerton, C.M. (n.d.) Stress and anxiety in athletics. United States Sports Academy America’s Sports
University, The Sport Digest. ISSN: 1558-6448.
4. Humac Norm. (n.d.) Retrieved from http://www.csmisolutions.com/products/isokinetic- extremitysystems/humac-norm
5. LeBlanc, V.R. (2009). The effects of acute stress on performance: Implications for health professions
education. Academic Medicine, 84(10), S25-S33.
6. Pilcher, J.J., & Huffcutt, A.I. (1996). Effects of sleep deprivation on performance: A meta- analysis.
Sleep, 19(4): 318-326.