Risk Factors for Depression in College Students
Abstract: Depression is an increasingly prevalent mental health issue, and most college
students are at the age of first onset. This study utilized logistic regression to build a model for
use in identification of depression in college students. In particular, it was determined that both
sleep and exercise interact, with lower levels of each indicating greater risk for depression in
this population. These findings are useful in identifying students at-risk for depression so that
preventative services might be offered to such students.
Background and Significance
Depression is a mental illness that can be debilitating to those who experience it. In addition to
psychological handicaps, depression can also affect a person’s cognitive ability and physical
health. Between 5 and 8% of Americans suffer from depression each year; only half of those
people are treated despite known treatments being substantially effective (Duckworth, 2013).
Therefore, it is important to develop new ways to identify people at risk for depression and offer
appropriate preventative treatment.
Current research has shown that genetics, stress, marital status, and drug and alcohol use all
play a role in depression (Haggerty, 2006). More recently, a European study of 12,395
adolescent students from 179 schools identified three additional factors that represent an
invisible risk for depression: sedentary behavior, inadequate sleep, and high levels of
technology use. Such behaviors are common among adolescents, yet often overlooked as risk
factors for depression (Carli, 2014).
As the average age of onset for depression is mid-20s it could be useful to apply these findings
toward identification of depression in college students (Haggerty, 2006). The present study
examines a sample of American college students with the goal of examining whether or not
college students follow the pattern that low levels of sleep and exercise, as well as high levels of
technology use, would result in an increased risk for depression. In addition, the study assesses
age, gender, GPA, truancy, alcohol use, and cigarette use as potential covariates.
Methods
Data were collected using Sona, an online experiment management system (Sona Systems,
2002). Participants received partial course credit as compensation for participation in this study.
They responded to an anonymous online survey which asked about various lifestyle habits and
depression-related symptoms. The sample consisted of 165 students enrolled in at least one
undergraduate psychology course at a mid-western university.
The dependent variable for analysis was depression level as assessed by the Patient Health
Questionnaire-9 (PHQ-9). The questionnaire consists of nine questions aimed at depression
related systems. Respondents indicate (on a scale of 0 = “not at all” to 3 = “nearly every day”)
how often they experience depression-related symptoms such as “Feeling down, depressed, or
hopeless.” The total score for these nine items places a respondent in one of the following five
categories: Minimal depression (0-4), mild depression (5-9), moderate depression (10-14),
moderately severe depression (15-19), or severe depression (20-27) (Kroenke, 2001). For the
purpose of this study, the scale was used to classify students as to whether or not they were at
least moderately depressed (10-27). Since the goal of this research was to identify students
who could be offered preventative services, this method served as a simple way to categorize
students as either at-risk or not at-risk.
The independent variables included three primary predictors: average hours of sleep per night,
average hours of exercise per week, and average hours of technology use per day. Additionally,
age, gender, GPA, major, number of classes missed per month, number of alcoholic beverages
consumed per week, and number of cigarettes smoked per week were included as possible
covariates.
The binary variable indicating moderate or greater depression was analyzed using logistic
regression. Backward elimination beginning with a model containing second order interactions
was used to achieve the final model. The significance level for removal was 0.05. The variables
considered for inclusion in the model were age, gender, GPA, missed classes, level of sleep,
level of exercise, technology use, alcohol use, and cigarettes use. Odds ratio confidence
intervals for significant terms are used to address the research questions described above.
Additionally, the probability of depression for various predictor values was also estimated using
the final model. All statistical analyses were conducted using Minitab and SAS software (Minitab,
2010; SAS, 2011).
Results
The logistic regression model treated
“depression” as the reference category.
Table 1 summarizes demographics
including age, GPA, and gender. There
were no significant differences on these
variables between groups.
Table 1. Demographics (n=165)
Mean/Count Std. Dev. Range/Percent
Age
GPA
Female
20.87
3.11
103
5.442
0.542
*
16 - 76
1.73 – 4.00
62.42%
Backward Elimination resulted in a final model containing sleep and exercise, as well as the
interaction between these two variables (see Table 2). While no other variables were significant
at the 0.05 significance level, there was evidence that technology might have some impact on
depression. The final model was assessed for goodness-of-fit using the Hosmer-Lemeshow test.
No evidence indicating lack of fit was found (x2 = 8.424, p-value = 0.393).
Table 2. Logistic Regression Model
Sleep
Exercise
Sleep*Exercise
Technology
Coeff.
SE
Z
P
-0.067
-0.714
0.132
-0.067
0.200
0.253
0.045
0.035
-0.33
-2.83
2.95
-1.93
0.739
0.005
0.003
0.053
Odds
Ratio
0.94
0.49
1.14
0.94
Lower
Limit
0.63
0.30
1.05
0.87
Upper
Limit
1.39
0.80
1.25
1.00
Due to the interaction between sleep and exercise levels, for interpretative purposes it is
necessary to examine odds ratios for sleep at different fixed levels of exercise. Likewise odds
ratios for exercise are examined at different fixed levels of sleep (see Table 3). Finally, Figure 1
displays the probability depression for various levels of sleep and exercise. The darker green
areas represent combinations of sleep and exercise levels that put a person at a higher risk for
depression.
Table 3: Odds Ratio Estimates (Wald Cis)
Exercise
OR
95% CI
OR
95% CI
at Sleep = 5
0.96
0.87 - 1.06
Sleep
at Exercise = 0
0.92
0.62 – 1.35
at Sleep = 6
1.09
0.99 - 1.21
at Exercise = 3
1.36
1.02 – 1.82
at Sleep = 7
1.25
1.07 - 1.46
at Exercise = 6
2.03
1.36 – 3.01
at Sleep = 8
1.43
1.13 - 1.80
at Exercise = 9
3.01
1.64 – 5.52
at Sleep = 9
1.63
1.19 - 2.23
at Exercise = 12
4.48
1.92 – 10.42
at Sleep = 10
1.86
1.24 - 2.77
at Exercise = 15
6.66
2.23 – 19.90
At low levels of sleep, the probability of
depression is high and there is no evidence that
increasing exercise affects risk for depression
(see Figure 1). However, at medium and high
levels of sleep, each additional hour of exercise
decreases the odds of depression. For example,
if a person gets 10 hours of sleep per night, for
each additional hour of exercise lowers the odds
of depression by a factor between 1.24 and 2.77.
Likewise, at low levels of exercise, the probability
of depression is high and there is no evidence that
additional sleep affects a person’s risk for depression. However, at medium and high levels of
exercise, additional sleep significantly decreases the odds of depression. For example, if a
person gets 9 hours of exercise per week, each additional hour of sleep per night will lower his
odds of depression by a factor between 1.64 and 5.52.
Discussion/Conclusions
This study hypothesized that low levels of sleep, low levels of exercise, and high levels of
technology would increase the risk of depression in college students. It was found that both
sleep and exercise do have the expected impact on risk of depression. Moreover, while
atypically low levels of either sleep or exercise will put a person at a higher risk for depression,
this study suggests that both must be increased to more “normal” levels in order for the odds of
depression to decrease. Furthermore, once normal levels are reached, the two factors have a
reinforcing interaction effect to further decrease the risk of depression.
Technology use was indicated as marginally significant in the model. A future study might
incorporate a larger sample to further assess this factor. A further limitation of this study is that
it did not distinguish between academic and non-academic technology use. Considering that
college students probably use technology for a large part of their schoolwork, we would expect
high levels in the sample. Future studies should specifically address the issue of whether or not
non-academic technology use is a factor.
In addition to the logistic model, depression scores (0-27) were also considered using multiple
linear regression (results not shown). This analysis was deemed not to be useful as the final
model correctly identified only 25% of truly depressed students – likely a result of the essentially
ordinal response. Future studies might consider whether an ordinal logistic model might be
used to fine-tune predictions related to different levels of depression.
Overall, the findings of this study were consistent with the previous literature. Students who
engage in low levels of exercise and sleep are at greater risk for depression than those with
adequate levels. While healthcare professionals may notice risky behaviors like drug and
alcohol use to be associated with depression risk, they may often miss students who do not
engage in these behaviors but are still at-risk. The present model did not find alcohol nor
tobacco use to be significant predictors of depression, perhaps because of low levels of use in
this sample. Given this information, it is even more important to identify the less obvious
behaviors that put students at risk for depression. It is suggested that the findings presented
here be used by a college health or wellness center to identify at-risk students and provide them
with prevention techniques.
References
Carli, V., Hoven, C. W., Wasserman, C., Chiesa, F., Guffanti, G., Sarchiapone, M., Apter, A.,
Balazs, J., Brunner, R., Corcoran, P., Cosman, D., Haring, C., Iosue, M., Kaess, M.,
Kahn, J. P., Keeley, H., Postuvan, V., Saiz, P., Varnik, A. and Wasserman, D. (2014). A
newly identified group of adolescents at “invisible” risk for psychopathology and suicidal
behavior: findings from the SEYLE study. World Psychiatry, 13, 78–86.
doi: 10.1002/wps.20088
Duckworth, K. (2013, April 1). What is Depression?. NAMI. Retrieved May 10, 2014, from
https://www.nami.org/Template.cfm?Section=depression
Haggerty, J. (2006). Risk Factors for Depression. Psych Central. Retrieved on May 10, 2014,
from http://psychcentral.com/lib/risk-factors-for-depression/00058
Kroenke, K., Spitzer, R., Williams, J. (2001). The PHQ-9: validity of a brief depression severity
measure. Journal of General Internal Medicine, 16, 606-613. doi: 10.1048/j.15251497.2001.016009606.x
Minitab 16 Statistical Software (2010). [Computer software]. Minitab, Inc. (www.minitab.com)
SAS Institute Inc. (2011). SAS System for Windows (Version 9.3) [Statistical
processing software]. Cary, NC: SAS Institute Inc.
Sona Systems, Ltd. (2002). [Software.] Available from: https://sona-systems.com