THE EFFECT OF SLEEP QUALITY ON REWARD MOTIVATION 
AND DEPRESSIVE SYMPTOMS IN ADOLESCENT GIRLS 
 
 
 
 
 
 
 
 
by 
MARIA LOUISE ROSE 
 
 
 
 
 
 
 
 
 
A THESIS 
 
 
 
 
 
Presented to the Department of Psychology 
and the Robert D. Clark Honors College  
in partial fulfillment of the requirements for the degree of  
Bachelor of Science 
 
March 2023 
  
An Abstract of the Thesis of 
Maria Rose for the degree of Bachelor of Science 
in the Department of Psychology to be taken June 2023 
Title: The Effect of Sleep Quality on Reward Motivation and Depressive Symptoms  
in Adolescent Girls 
 
Approved:   Melynda Casement, Ph.D.   
Primary Thesis Advisor 
 
 Depression is one of the leading causes of global disease, (Kessler et al., 2005) and 
adolescents face a higher likelihood of diagnosis (Fleming and Offord, 1990). Because of its 
prevalence and recurrent nature (Pine et al., 1999), factors related to depression have been 
researched for many years. Sleep health is one of the many factors established to be a correlate of 
depressive disorders. While worse sleep is known to increase the risk of depressive 
symptomatology (Nutt, Wilson & Paterson, 2022), less is understood about the mechanisms that 
mediate this relationship. Connected to both sleep and depression are reward processes (Nestler 
& Carlezon, 2006; Shankman et al., 2007; Bress et al., 2012; Heshmati & Russo, 2015). One 
particular aspect of reward processing is reward motivation, or desire-driven action for a reward. 
Because depression rates in adolescent women are especially prevalent, finding risk factors and 
providing adequate intervention for symptom onset is a noble goal. My aim is to find the 
associations between sleep quality, depressive symptoms, and reward motivation. More 
specifically, I aim to determine if reward motivation mediates sleep quality and depression 
symptomatology. After screening criteria were met, data were collected from 120 adolescent 
girls. Of this sample, 65% were Black, 27% were White, and 8% were multi-racial (Casement et 
al., 2019). Through a series of linear regressions, I found nonsignificant relationships between 
the variables of depression, sleep quality, and hypothesized mediating factor of reward 
motivation. Results from this study aim to provide context for depression prevention and 
intervention and future research direction. 
  
2 
 
Acknowledgments 
 
 
I would first like to thank Dr. Melynda Casement for her unwavering support and 
continuous assistance throughout this project. Her expertise in the field of sleep research and 
psychopathology continues to inspire me, and I would not have been able to complete this 
project without her kindness, patience, and wide scope of knowledge. I would also like to thank 
my two other committee members, Dr. Nicole Dudukovic and Aubrey Rossi, who have 
generously dedicated their time and understanding to assist me in the completion of my thesis. I 
also am extremely grateful for the guidance of my Clark Honors College (CHC) advisor, Dr. 
Casey Shoop, who has met with me routinely throughout my CHC journey to ensure my success. 
I would also like to thank my friends -- for their kindness, assistance in revisions, and laughs 
along the way. Lastly, I would like to thank my parents for their constant encouragement and 
support in the pursuit of my degree. Without them, this journey would not have been possible. 
Thank you, all.  
3 
 
Table of Contents 
Chapter 1: Introduction………………………………………………..…………………………..7 
Chapter 2: Methods………………………………………………………………………………12 
 A: Participants……………………………………………………………………………12 
 B: Materials………………………………………………………………………………12 
Chapter 3: Procedure……………………………………………………………………………..16 
Chapter 4: Results………………………………………………………………………………..18 
 A: Sleep quality and depression……………………………………………………….…18 
 B: Sleep quality and reward motivation…………………………………………….……19 
 C: Mediation Analysis…..……….…….……………….…………….………………… 19 
Chapter 5: Discussion………..………………….…………………………………………….…21 
 A: Reward motivation and sleep quality…………………………………………………21 
 B: Sleep quality and depression……………………………………………………….…22 
 C: Limitations and future directions……………………………………………………..23 
Chapter 6: Figures………………………………………………………………….…………….25 
Chapter 7: Bibliography………………………………………………………………………….32 
  
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List of Figures 
Figure 1: Participant recruitment flow chart……………………………………………….……25 
Figure 2: Overview of EEfRT procedure……………………………………………….…….…25 
Figure 3: Anhedonia and hard task choice……………………………………………..……..…26 
Figure 4: Display of sleep quality effect on depressive symptoms…………..………………….26 
Figure 5: Display of sleep quality effect on reward motivation……………………………....…27 
Figure 6: Display of reward motivation’s effect on sleep quality and depressive symptoms.…..27 
Figure 7: Descriptive characteristics of age, CESD, and PSQI scores……….……….…………28 
Figure 8: Descriptive characteristics………………………………….…………….……………29 
Figure 9: Scatterplot depicting sleep quality and depression scores in adolescent girls…..….…30 
Figure 10: Scatterplot depicting sleep quality and reward motivation in adolescent girls………31 
5 
 
Chapter 1: Introduction 
Major Depressive Disorder (MDD) is a relatively common mood disorder and is 
characterized by a persistent, low mood lasting at least two weeks. Symptoms of MDD can 
include lethargy, appetite changes, weight fluctuation, sleep disruptions, feelings of 
worthlessness, and sometimes thoughts of suicide (American Psychiatric Association, 2013). 
People who are diagnosed with MDD will also often experience a loss of interest in activities 
they used to enjoy (Judd, Akiskal, & Paulus, 1997). Furthermore, Kessler et al. (2005) report that 
approximately 16.6% of adults are diagnosed with MDD within their lifetime.  
Critically, adolescents are at a greater risk for diagnosis of MDD (Saluja et al., 2004). In 
a school survey of students ages 11-15, 18% of participants self-reported symptoms of MDD. 
Moreover, a diagnosis of depression during adolescence strongly predicts the presence of 
depression in adulthood (Pine, Cohen, Cohen, and Brooke 1999). Not only is depression most 
prevalent amongst adolescents, but it is also disproportionately distributed between the sexes. Of 
those who report depressive symptomatology, a greater proportion of young women (25%) were 
affected than young men (10%), and from the beginning of puberty onward, rates of depressive 
disorders are two to three times more common in women than in men (Saluja et al., 2004; 
Leibenluft, 1999). One research extension from Kessler et al. (2005) found that the lifetime risk 
of any mood disorder is 1.5 times higher for women.  
Unfortunately, adolescent depression is frequently ignored or misunderstood, with its 
symptomatology incorrectly explained through teenage angst or other disorders (i.e. conduct, 
attentional, or substance use disorders). As depression is highly correlated with the risk of 
suicide, this mischaracterization can have potentially fatal consequences (Birmaher et al., 1996). 
Alongside the increased risk of suicide, those who experience symptoms of depression at an 
6 
 
early age are far more likely to struggle with the disorder throughout their lives (Lewinsohn et 
al., 1999). Depression is also associated with psychopathological comorbidities including 
anxiety, conduct disorders, substance abuse disorders, and risk behaviors such as having 
unprotected sex (Brooks et al., 2002). Depression also affects interpersonal relationships with 
correlations found between diagnoses and fights with peers (Ryan et al., 1987) alongside deficits 
in overall physical health (Beekman et al., 1997).  
There are a variety of potential explanations for depressive symptoms, however, one 
particular correlate is sleep. Within the past twenty years, poor sleep has been reconceptualized 
as a risk factor for the onset of mental health difficulties and disorders. For example, sleep 
difficulty has been established as a correlate for negative affect (Finan et al., 2015), increased 
burnout (Vela-Bueno et al., 2008), social isolation (Yu et al., 2018), increased loneliness (Xu et 
al., 2012), and deficits in cognitive ability (McCoy & Strecker, 2011). The relationship between 
poor sleep and worse mental health also translates to adolescent populations. A longitudinal 
study published in 2009 determined that sleep disruption in adolescence is correlated with later 
declines in mental health (Kaneita et al, 2009). 
Critically, sleep has a profound association with depressive symptoms. In fact, 
approximately 90% of people diagnosed with MDD have experienced some type of sleep 
disturbance (Geoffroy, et al., 2018). Furthermore, researchers Breslau et al. (1996) found that 
complaints of insomnia for two weeks or more were a marker for the onset of MDD in their 
study with adult participants. Moreover, research from Chang et al. (1997) demonstrates that the 
effects of poor sleep persist years after it is first recognized. Through a longitudinal study of 
graduating men, researchers found that complaints of insomnia were associated with an 
increased risk for MDD and psychological distress 34 years later. Just as there is evidence that 
7 
 
poor sleep worsens depression symptoms, researchers have also found that treatment for poor 
sleep mitigates depression. For example, in a study by Cheng et al. (2019), researchers found that 
the treatment of insomnia through digital cognitive behavioral therapy mitigated depression 
symptoms a year following the treatment.  
 Ties between sleep and depression exist in populations of mentally healthy patients as 
well as adolescents. In 2014, researchers Tkachenko et al. found that neurotypical subjects who 
reported sleep disruption also reported anxiety and depression at increased rates. Working 
alongside a sample of adolescents, Roberts and Duong (2013) found that insomnia increased the 
risk of clinical depression 2-3-fold. In correspondence with these findings, researchers have 
discovered successful sleep interventions that limit the effects of worse sleep on depressive 
symptoms. Researchers Casement et al. (2020) randomly assigned two groups of depressed 
female adolescents to a typical sleep or sleep extension group. Through actigraphy, sleep diary, 
and subjective sleep data, they found that a sleep extension of just one hour decreased symptoms 
of both depression and anhedonia. 
 One specific measure of sleep is subjective sleep quality. Like the previously mentioned 
measures of sleep, sleep quality is also correlated with depressive symptoms. Illustrating this, 
Norra et al. (2012), found that participants with worse sleep quality had more symptoms of 
depression and that improving sleep may improve depressive symptoms. Worse sleep quality is 
also correlated with suicidality. A study by Aǧargün, Kara, & Solmaz (1997) found that worse 
subjective sleep quality is associated with increases in suicidal behavior. Given the strength of 
the relationship between poor sleep quality, depression, and suicidal symptoms, mediators of 
sleep and depression must be investigated.  
8 
 
 Reward motivation could play a key role in the mediation of these two factors. It is well 
established that reward motivation and sensitivity are highly associated with depression 
symptomatology (Nestler & Carlezon, 2006; Admon & Pizzagalli, 2015; Shankman et al., 2007; 
Boland et al., 2020). One key symptom of depression is a lack of pleasure or anhedonia. While 
there are several biomarkers of reward response, researchers Bress et al. (2012) addressed reward 
response specifically in feedback negativity (FN). Similar to adult samples, higher scores of 
depression in adolescence were correlated with decreased FN response. Further research, like 
that from Heshmati & Russo (2015), has provided a more concrete biological framework for 
reward, detailing the role of the nucleus accumbens and surrounding neuroanatomy activated in 
reward sequences but dysregulated in the presence of depression.  
 Central to the subject matter of this paper, the relationship between depression and 
reward generalizes to adolescents. Through a verbal memory task with varied rewards, 
Henriques & Davidson (2000) evaluated reward motivation between depressed and non-
depressed individuals. They found that depressed adolescents had a decreased response to 
rewards. This is consistent with the literature surrounding the nature of anhedonia in depressed 
individuals. These findings are also consistent with the leading hypothesis that the left prefrontal 
hypoactivation in depressed populations reflects a decrease in approach-related behavior.  
 Less is understood about the relationship between sleep quality and reward, however, 
preliminary research demonstrates that worse sleep is associated with disruptions in multiple 
reward systems. Through an fMRI, researchers Holm et al. (2009) found that during the 
anticipation of a reward, less activation in the caudate nucleus was correlated with decreased 
sleep duration, sleep onset time, and worse sleep quality. During reward outcome, less activation 
in the caudate was again correlated with later sleep onset time, earlier sleep offset time, and 
9 
 
lower sleep quality. Other literature has established relationships between sleep disturbance and 
decreased effort expenditure for reward (Engle-Friedman et al., 2010) alongside worse sleep 
quality and diminished reward responsiveness (Wieman et al., 2022). Lastly, a study by 
Casement et al. (2016) found a mediating effect of the neural activity preceding a reward in 
regard to nonrestorative sleep and adolescent depression. Altogether, these findings suggest that 
sleep disruption is tied to deficits in reward processing, and that reward motivation may play a 
significant role in understanding the relationship between sleep quality and depression. 
 Previous studies provide a substantial knowledge base of sleep, depression, and reward 
processing. This literature describes the interrelationships between depression, its comorbidities, 
and persistence across both gender and age groups, alongside evaluating worse sleep as a 
predictor of both depression and suicidality. Furthermore, it lays the foundation for how poor 
sleep affects mood, and how sleep disruption impacts reward anticipation, motivation, and effort 
expenditure. What has yet to be investigated is the potentially mediating factor of reward 
motivation between sleep quality and depression. In the current study, women were specifically 
sampled due to their heightened rates of depression prevalence and the need for targeted 
intervention. The present study aims to investigate reward motivation as a mediator of poor sleep 
quality and depressive symptoms within this population.   
10 
 
Chapter 2: Methods 
Participants 
 The participants of this study were young women, ages 18-22 years old. These women 
suffered both insufficient sleep and moderate to high scores of depressive symptoms. Insufficient 
sleep was determined by a score of daytime ≥6 on the Epworth Sleepiness Scale (Johns, 1999) or 
a score of ≥16 on the PROMIS Sleep-Related Impairment Scale (Yu et al., 2012,) alongside 
regularly sleeping less than 8 hours per night. Depression was operationalized through the Center 
for Epidemiological Studies for Depression Scale (CESD; Radloff, 1977), and the inclusion 
criteria for this study was a score ≥15, which is slightly above the average score in young adults 
(Radloff, 1991). 
 Exclusion criteria included the diagnosis of a sleep disorder (e.g. insomnia, sleep apnea, 
hypersomnia), psychotic symptoms, and substance use disorder. Study exclusion was also based 
on present suicidal ideation, use of psychiatric medications, or a history of a neurological 
disorder. These conditions aimed to exclude participants whose sleep extension would be 
ineffective or contraindicated as well as control for comorbidities in psychopathology that could 
present challenges to sleep. Exclusion criteria also worked to control for variance in response 
that could not be modeled with a small sample size.  
 
Materials 
 Adolescent psychiatric disorders: The Structured Clinical Interview for DSM 5 
Psychiatric Disorders (SCID; First, Williams, Karg, & Spitzer, 2015) was used to screen for 
psychotic symptoms, and diagnose mood, anxiety, substance use, and trauma-related disorders. 
These diagnostic interviews were provided by a postbaccalaureate research coordinator or a 
11 
 
clinical psychology doctoral student. All interviewers were trained and supervised by Dr. 
Melynda Casement. 
 Adolescent depression: Depression was measured through the CESD (Radloff, 1977). 
The CESD is comprised of 20 self-reported Likert-style questions that measure mood and 
behavior over the past week. Some of these questions include “I was bothered by things that 
usually don’t bother me,” “I talked less than usual,” or “I felt lonely.” Participants then indicate 
how often they have felt that way on a 0-4 scale — 0 specifying “Rarely or none of the time (less 
than 1 day)” and 4 indicating “most or all of the time (5-7 days).” After completion of the scale, 
scores are added. These scores range from 0-60, and a score of 16 or higher is considered 
depressed. Epidemiological data specify a mean CESD score of 12.51 (SD = 9.41) in adolescents 
(Radloff, 1991). The CESD has high internal consistency (α = 0.79–0.87; Radloff, 1977; Radloff, 
1991) and 2-week test-retest reliability (0.51; Radloff, 1977). 
 Subjective sleep characteristics: Past month's sleep quality was measured through the 
Pittsburgh Sleep Quality Index (PSQI; Buysse, Reynolds, Monk, Berman, & Kupfer, 1989). The 
PSQI is an 18-item questionnaire with seven component scores ranging from 0 to 3. These result 
in an overall sleep quality score (range 0–21). A score of five or greater indicates a high 
probability of sleep disorder (Buysse et al., 1989). The PSQI is internally valid (α = 0.80–0.85), 
has high test-retest reliability (r = 0.85–0.87) and concurrent validity with other assessments (r > 
0.69; Backhaus, Junghanns, Broocks, Riemann, & Hohagen, 2002; Buysse et al., 1989; 
Carpenter & Andrykowski, 1998). 
 Sleep disorders: Sleep disorders including but not limited to insomnia, hypersomnia, 
sleep-disordered breathing, circadian rhythm, and sleep movement disorders were measured with 
the Structured Clinical Interview for DSM 5 Sleep Disorders (SCIDSLD; Wong, Samuelsson, 
12 
 
Casement, & Buysse, 2016). The SCIDSLD is comprised of screening questions that include “do 
you have excessive daytime sleepiness?” and “do you feel excessively sleepy even when you 
have enough opportunity to sleep at night?” If a participant agrees with a statement, the 
interviewer proceeds to the next indicated module to ask more refining questions with regard to 
episode history, insomnia, hypersomnolence, narcolepsy, circadian rhythm sleep-wake disorders, 
non-rapid eye movement sleep arousal disorders, nightmare disorder, restless leg syndrome, and 
substance/medication-induced sleep disorder. These diagnostic interviews were provided by a 
post-baccalaureate research coordinator or a clinical psychology doctoral student. All 
interviewers were trained and supervised by Dr. Melynda Casement. 
 Reward motivation: Reward motivation was measured through The Effort Expenditure 
for Rewards Task (EEfRT; Treadway et al., 2009). The EEfRT is a multi-trial game in which 
participants choose between two tasks varying in difficulty to obtain monetary rewards. During 
each trial, participants repeat manual button presses within short successions of time. More 
difficult tasks are rewarded with higher compensation. For example, successful completion of the 
difficult task required 100 buttons pressed with the non-dominant pinky within 21 seconds, while 
completion of the easy task trials simply required the participants to make 30 button presses 
using the dominant pointer finger within 7 seconds. To aid participants in choosing which trials 
they were most likely to win, participants were presented with probability cues at the beginning 
of each trial, ranging from high (e.g. 88%) to low (e.g. 12%). After making a decision, 
participants' screens displayed a 1-second “ready” screen, then they completed the task. 
Afterward, participants were shown a 2-second feedback screen that displayed if the task was 
successfully or unsuccessfully completed. If the task was successfully completed, then a second 
feedback screen appeared for 2 seconds which displayed their monetary prize value (See Figure 
13 
 
2). On average, easy-task trials took approximately 15 seconds, and hard-task trials took 
approximately 30 seconds.  
 The EEfRT model explored interactions between anhedonia and probability and reward 
magnitude. A pattern was found in the initial study by Treadway et al. (2009) that those with 
higher levels of anhedonia were far less likely to choose a hard task (See Figure 3).  
14 
 
Chapter 3: Procedure 
 Written informed consent was gathered from all participants, and participants were 
debriefed following all study procedures. Participants received up to $361 for their time and 
participation. Procedures were approved by the Institutional Review Board.  
 Participants were recruited through advertisements distributed on social media platforms 
and campus email listservs. Those who were interested registered on a web-based participant 
recruitment platform and completed a short pre-screening questionnaire. Of those who met 
gender and age inclusion criteria, participants then completed additional screening assessing 
sleep characteristics, and symptoms of depression. Those who continued to meet inclusion 
criteria were then invited to complete a telephone screening. During this assessment, the study 
coordinator provided select items from the SCIDSLD to exclude participants who likely had a 
sleep disorder. Those who remained eligible were then invited to complete an in-office screening 
for sleep and psychiatric disorders using the SCIDSLD and SCID, respectively.  
 During baseline sleep monitoring, bedtimes and wake times were created based on the 
participants’ respective typical sleep duration plus an additional 30 minutes (obtained during 
screening). Participants were instructed to keep to a regular sleep schedule within the parameters 
that both accommodate their daytime obligations and remained similar to their typical sleep 
timing. During this time, participants received automated text reminders (30 minutes before 
bedtime and 30 minutes after wakeup) about the sleep monitoring protocol, and a link to 
complete the daily sleep diaries and questionnaires. Sleep diary entries and actigraphy data were 
routinely reviewed to monitor for adverse reactions. Participants who deviated significantly from 
their sleep schedule were removed from the study and were compensated for their participation. 
15 
 
 After baseline observation, participants were randomly assigned to the typical sleep 
opportunity (TSO) or extended sleep opportunity (ESO) condition. At this time, participants 
received additional instructions for their sleep schedule. ESO participants were asked to increase 
their sleep opportunity by 90 minutes, while TSO participants were asked to continue with the 
same sleep schedule as the previous week. ESO condition participants could increase their sleep 
opportunity by either advancing their bedtime, delaying their wake time, or taking naps. These 
participants were also instructed to contact the study investigator if they experienced difficulty 
falling asleep or staying asleep during this extension phase. 
 To investigate the hypothesized mediating effect of reward motivation on depression 
symptomatology and sleep quality, I planned to utilize regression analysis. This analysis was 
composed of three regression sets: X → Y, X → M, and X + M → Y. X → Y: See Figure 4; X → 
M: See Figure 5; X + M → Y: See Figure 6.  
16 
 
Chapter 4: Results 
 
Figure 1 displays a flowchart depicting participant exclusion and withdrawal, and 
Figures 7 and 8 provide descriptive characteristics for those who completed the 2-week sleep 
protocol (N = 22). The breakdown of demographics for those who began the sleep protocol is as 
follows. Of the 11 individuals randomly assigned to the typical sleep, control group, M age = 
20.41, SD = 1.24. 8 (72.7%) of these individuals were White, 1 (9.1%) of them were Asian, and 
2 (18.2%) of them were other. Of the other 11 individuals randomly assigned to the extended 
sleep, experimental group, M age = 19.58, SD = 1.02. Of these individuals, 7 (63.6%) of them 
were White, 1(9.1%) was Asian, 2 (18.2%) were other, and 1 (9.1%) were multiracial. Of those 
who withdrew from the study (N = 10), M age = 20.52, SD = 1.34. Of these 10, 5 (50%) were 
White, 2 (20%) were Asian, 1 (10%) was other, and 2 (20%) were multiracial. The average 
participant was 20 years old, enrolled in college (84%), had at least one parent with a college 
degree (75%), and was Caucasian (62.5%). Furthermore, 56% of this sample was diagnosed with 
a lifetime mood disorder diagnosis (Casement et al., 2021).  
Of the 11 individuals randomly assigned to the typical sleep control group, CESD scores 
(M = 18.18, SD = 9.61) were higher than those who completed the sleep extension protocol (M = 
13.55, SD = 7.98) following the sleep duration manipulation. PSQI scores also varied between 
groups with the control group reporting worse overall sleep quality (M = 7.18, SD = 2.18) than 
the experimental group (M = 6.36, SD = 1.50) after sleep manipulation. 
Sleep quality and depression: A linear regression was used to test if worse sleep quality 
predicts higher depression scores. Results showed that there was not a significant relationship 
between sleep quality and depression scores (R2 = 0.0151, F(1, 20) = 0.307, p = 0.585). The 
17 
 
effect size of this relationship was small such that depression (as measured by CESD on the 2nd 
visit) accounted for just 1.513% of the variability in sleep quality. These results indicate that as 
subjective sleep quality worsens, depression scores do not necessarily increase. This does not 
support the hypothesis that worse sleep quality would predict increased depressive 
symptomatology in adolescent girls. See Figure 9 for a scatterplot depicting the relationship 
between sleep quality and depression scores. 
Reward motivation and sleep quality: A second linear regression was used to test the 
relationship between sleep quality and reward motivation, operationalized by the mean 
proportion of hard tasks chosen throughout EEfRT trials. The results of this regression displayed 
a nonsignificant relationship between sleep quality and reward motivation (R2 = 0.0404, F(1, 19) 
= 0.799, p = 0.383). Again, the effect size was minimal such that reward motivation accounted 
for 4.04% of the variability in sleep quality. Indicating a nonsignificant relationship between 
variables, these results do not support the hypothesis that worse sleep quality would predict a 
lower proportion of hard tasks chosen. See Figure 10 for a scatterplot depicting the relationship 
between sleep quality and reward motivation.  
Mediation analysis: This analysis aimed to explore the impact of sleep quality on 
depression as mediated by reward motivation. It was hypothesized that having worse sleep 
quality would positively predict depression. Furthermore, it was predicted that reward motivation 
would be a mediating variable. A series of regression analyses were used to test these 
hypotheses. The results demonstrate a nonsignificant mediation relationship such that B = -
0.0460, z = -0.705, p = 0.481 (95% CI, -0.832 to 0.392). Alongside this, results showed that sleep 
quality had a nonsignificant relationship with reward motivation (B = 0.2009, z = 0.940, p = 
0.347), and that reward motivation had a nonsignificant relationship with depression scores (B = 
18 
 
-0.2290, z = -1.066, p = 0.286). Direct effects were also null; sleep quality had a nonsignificant 
relationship with depression scores (B = 0.1894, z = 0.882, p = 0.378).   
19 
 
Chapter 5: Discussion 
Through regression analyses, the current study found no significant associations between 
sleep quality, depression, and reward motivation. Furthermore, reward motivation did not 
mediate the relationship between sleep quality and depression. These results did not support the 
hypothesis that these factors would be both correlated and also mediated by reward motivation.  
As stated before, the relationship between sleep quality and reward motivation was 
nonsignificant. In analyzing the relationship between these variables, the line of best fit is not 
only inconsistent with the initial hypothesis, but it follows the opposite direction, such that as 
sleep quality worsens, subjects chose a higher proportion of hard tasks. This could potentially be 
explained by an increased drive for reward following worse sleep. 
 In 2013, researchers Telzer et al. (2013) sought to explain the relationship between poor 
sleep quality and reward-related brain function in adolescents. Their results demonstrated that 
subjects with poorer sleep also experienced increased reward motivation, also noting that this 
relationship is connected to decreased cognitive control and increased risk-taking behaviors. 
Moreover, Telzer et al. (2013) describe that poor sleep may exaggerate the already imbalanced 
relationship between affective and cognitive control systems. These findings could explain the 
mechanism by which increased risk-taking behaviors occur in adolescents with worse sleep. 
Increased reward motivation following poor sleep quality was also observed in a population of 
pregnant and postpartum mothers. Betts et al. (2021) found that depressive symptoms, stress, and 
worse sleep quality were all associated with increased hedonic eating, or the desire to eat foods 
in the absence of hunger cues.  
The work of Betts et al. (2021) corresponds with existing literature that explores the role 
of poor sleep in relation to reward motivation. In a paper from Gujar et al. (2011), researchers 
20 
 
iterate that poor sleep and sleep deprivation contribute to reward reactivity and motivation. 
Researchers demonstrate that these effects can be witnessed through fMRI across activation 
differences. Critically, Gujar et al. (2011) find that sleep deprivation activates increased activity 
in the mesolimbic system, a key dopaminergic pathway that responds to pleasure and rewards. 
Researchers also describe how this activation enhances response to positive emotional stimuli 
(Gujar et al., 2011) and negative emotional stimuli (Yoo et al., 2007). Yoo et al. (2007) report 
that this activation in the amygdala is correlated with decreased activity in the medial prefrontal 
cortex.  
With increased activation in areas of the brain that process emotional stimuli and under 
activation of the prefrontal cortex and other cognitive control systems, the results of these 
articles offer a foundation for understanding the consequences and risks of reduced sleep and 
poor sleep quality. Consistent with these findings, the results of the current study demonstrate a 
slight trend whereby as adolescent women worsen in sleep quality, they are more likely to 
choose a harder task for an increased reward. Because poor sleep quality is pervasive in 
adolescents (Saxena, Koreti, & Gaur, 2016), its effects on reward motivation and consequences 
related to risky decision-making and decreased cognitive control must be addressed. In the same 
way, researchers Harrison and Horne (2000) mark poor sleep as not only a developmental 
concern but also a public health issue that impacts adolescent well-being. The physical, 
emotional, and social developments during adolescence are indispensable. Being that decreased 
sleep quality is associated with markers of decreased cognitive control and risky decision-
making, it is important that adolescents increase sleep and overall sleep quality to promote their 
well-being.  
21 
 
Unlike sleep quality and reward, the relationship between sleep quality and depression 
follows the hypothesized direction of correlation, but not to a significant extent. This could be 
due to the limited age range of this study’s sample, only including women ages 18-22. According 
to Villarroel & Terlizzi (2020), depression prevalence is highest among those aged 18-29. 
Furthermore, a study from Madrid-Valero (2017) found that increases in age are directly related 
to decreases in subjective sleep quality, especially among women. Thus, including ages 23-29 in 
this sample could impact mean CESD scores and the interrelationship between depression and 
sleep quality. Lastly, expanding this study's sample size could increase generalizability and 
provide context for future interventions within this group.  
This study was not without its limitations. The primary limitation of this study was its 
small number of participants (N = 22). Having too small of a sample increases the margin of 
error, and reduces the statistical power of a study. Alongside having a smaller sample size, this 
study’s age range was limited. Participants were all young women, aged 18-22. Because of its 
limited range, age may have modified results. Another limitation of the present study was the 
restricted manipulation of the PSQI. In Casement et al’s (2020) study, sleep duration was the 
only component of the PSQI that was manipulated. 
In an effort to mitigate the limitations of the present study, directions for future research 
could include an increased sample, participants from a wider age group, increased manipulation 
of PSQI components, and the search for methods to promote sleep health within this age group. 
In increasing sample size, future researchers could utilize greater statistical power and better 
controls of sample variance, however, having too large of a sample may inflate small differences 
within the study. Secondly, due to the increased prevalence rates of depression in younger 
adolescents, future studies may wish to include a wider breadth of age groups, including but not 
22 
 
limited to a wider range of adolescents or greater youth (10-24). Furthermore, although 
Casement et al. (2020) found that increasing sleep duration is associated with diminished 
depressive symptoms, it may be worthwhile to manipulate more components of sleep quality to 
better understand how depression varies with sleep disturbance, sleep latency, daytime 
dysfunction due to sleepiness, sleep efficiency, overall sleep quality, and sleep medication use. 
Knowing the prevalence and risk of depression and diminished sleep quality particularly among 
adolescents, directions for future research could also include finding methods, clinical or 
otherwise, to promote increased sleep health within this age group. Depression is one of the 
leading causes of global disease (Kessler et al., 2005), and its diagnosis is associated with the 
risk of suicide (Birmaher et al., 1996), anxiety, conduct disorders, substance abuse disorders, 
risky behaviors (Brooks et al., 2002), deficits in interpersonal relationship, and physical health 
(Beekman et al., 1997). With poor sleep as a marker for MDD (Geoffroy, et al., 2018), and ties 
between both of these variables and reward response (Treadway et al., 2012; Luking et al., 2016; 
Holm et al., 2009); Wieman et al., 2022), finding successful sleep and depression interventions 
for adolescents is paramount.   
23 
 
Chapter 6: Figures 
 
Figure 1: Participant recruitment flow chart (Casement, Livingston, Allen, & Forbes, 
2020) 
 
Figure 2: Overview of EEfRT procedure (Treadway et al., 2009) 
24 
 
 
Figure 3: Anhedonia and hard task choice (Treadway et al., 2009) 
 
Figure 4: Display of sleep quality effect on depressive symptoms 
25 
 
 
Figure 5: Display of sleep quality effect on reward motivation 
 
Figure 6: Display of reward motivation’s effect on the relationship between sleep quality and 
depressive symptoms 
26 
 
 
Figure 7: Descriptive characteristics of age, CESD, and PSQI scores 
 
27 
 
Figure 8: Descriptive characteristics of race, employment status, education, gender, sexual 
orientation, and ethnicity 
 
Figure 9: Scatterplot depicting the relationship between sleep quality and depression scores in 
adolescent girls 
28 
 
 
Figure 10: Scatterplot depicting the relationship between sleep quality and reward motivation in 
adolescent girls  
29 
 
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