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. Author manuscript; available in PMC: 2026 Mar 13.
Published before final editing as: Behav Sleep Med. 2026 Feb 14:1–13. doi: 10.1080/15402002.2026.2628676

Relations Among Sleep, Internalizing Symptoms, and Academic Performance in College Students

Janine Shuman a, Shabeeba S Sheikh b, Ivan Vargas c, Matthew K Robison c
PMCID: PMC12980802  NIHMSID: NIHMS2149657  PMID: 41689506

Abstract

Objectives:

To examine correlations among sleep, internalizing symptoms, and academic performance among undergraduate students.

Methods:

A sample of undergraduate students (N = 255) at a comprehensive public university in the U.S. wore a Fitbit activity band for three weeks to track their sleep via actigraphy. Average total sleep time, circadian midpoint, and sleep efficiency metrics, as well as intraindividual variability in those metrics, were used. Participants also completed a subjective sleep quality questionnaire and self-assessments of depression and anxiety symptoms. Academic performance (i.e., GPA) was gathered from the university registrar’s office.

Results:

There were mostly near-zero relations among actigraphy sleep metrics, academic performance, and internalizing symptoms in the sample. However, there were significant correlations among subjective sleep quality and internalizing symptoms.

Conclusion:

Contrary to our hypotheses, students who slept little, poorly, or with night-to-night variability, as measured by wearable actigraphy, did not have elevated symptoms of anxiety and depression, nor poorer academic performance.

1. Background

The prevalence of both sleep disturbance and mental illness are pressing public health concerns. Approximately 23% of all Americans report having a mental illness in the past year (Substance Abuse and Mental Health Services Administration, 2022) with young adults (age 18 – 25) reporting the highest prevalence rates (36%). The two most common forms of psychopathology among college students are depression and anxiety (Pedrelli et al., 2015), which, as highly comorbid sets of symptoms, may represent a common latent internalizing dimension (Kotov et al., 2017). Therefore, for the purposes of the present investigation, we will collectively refer to anxiety and depression as internalizing symptoms. In a recent survey of students across several institutions, 40%reported suffering from at least one mental health problem in the past academic year, and 34% reported moderate to severe anxiety symptoms (Lipson & Eisenberg, 2018). Furthermore, according to a systematic review that included data from six different countries, on average, 18.5% of college students report insomnia (Jiang et al., 2015). A more recent study reported that both insomnia and behaviorally induced insufficient sleep syndrome (volitional short sleep with daytime impairment) are highly prevalent among college students, 22% and 10% respectively (Williams et al., 2020). Internalizing symptoms and sleep disturbance, both independently and in combination, have functional implications for an individual’s relationships, occupational performance, and academic achievement (Kucharczyk et al., 2012; Weidman et al., 2015).

Sleep disturbance and internalizing symptoms frequently co-occur, exhibiting a complex and bidirectional relationship in which each adversely affects the other, particularly in cases of depression and anxiety disorders (Alvaro et al., 2013). While insomnia often precedes and predicts depression symptoms (Baglioni et al., 2011; Hertenstein et al., 2019), anxiety symptoms often lead to difficulties falling and staying asleep (Ali & Viqar, 2024). If sleep disturbance and internalizing symptoms are indeed interrelated, then treating insomnia could potentially improve anxiety and depression symptoms. Several studies, for example, have shown that cognitive-behavioral therapy for insomnia (CBT-I) improves and even prevents depressive symptoms (Carney et al., 2017; Cheng et al., 2019).

In a study of undergraduate students in the U.S., individuals suffering from moderate levels of depression displayed a significant negative correlation with their cumulative grade point average (GPA), supporting that even mild depression symptomatology may be a meaningful predictor of lower academic performance (Turner et al., 2012). Anxiety has also been found to correlate with academic performance, though the available research on its impact is sparse and inconsistent. Gorman et al. (2020), for example, found that greater anxiety symptoms were associated with higher course dropout rates among undergraduate students.

Different dimensions of sleep have been shown to be related to academic performance, as well, including total sleep time, sleep and wake times, and sleep disruptions. In a study of undergraduate students in the U.S., early sleep and wake time correlated strongest with higher academic achievement in terms of cumulative GPA, even more so than total sleep time (Eliasson et al., 2010). Creswell et al. (2023) found consistent negative correlations between total sleep time and semester GPA (see also Okano et al., 2019). Gaultney (2010) also found that students with greater sleep disturbance were significant more likely to be in struggling academically (i.e., GPA < 2.0). Hartmann and Prichard (2018) also found a greater likelihood of dropping a course and lower GPAs among students who experienced sleep problems. Within a large sample of college students, Taylor et al. (2013) found that students reaching clinical criteria for chronic insomnia reported greater depression, anxiety, stress, and lower quality of life compared to other participants. However, they did not find a difference in academic performance between these groups (Taylor et al., 2013). That said, overall, there is evidence to support that poor sleep tends to negatively affect academic performance in college students.

There has been a recent surge of interest in not only average sleep durations, sleep/wake times, and sleep efficiency, but also the intraindividual variability (IIV) of these metrics (Bei et al., 2016; Dillon et al., 2015; Fuligni & Hardway, 2006). More recent work has also demonstrated relations among intraindividual variability (IIV) in sleep, affect, mental health, and academic performance. Elevated IIV in total sleep time (i.e., greater within-person variability in daily total sleep time) has been associated with greater self-reported stress (Gao & Scullin, 2024, Mezick et al., 2009; Veeramachaneni et al., 2019), negative mood (Bei et al., 2017), and elevated risk for depression (Slavish et al., 2019). Students who report more night-to-night variability in sleep and wake times had significantly lower GPAs (Phillips et al., 2017). Similarly, Dietch et al. (2023) found that IIV in total sleep time, after accounting for covariates like GPA, stress, sleep efficiency, and IIV in sleep efficiency, was a significant predictor of college graduation. Therefore, we investigated both average sleep metrics and IIV therein in relation to internalizing symptoms and academic performance.

Based on the research cited above, we found there to be a distinct lack of research relating objective measures of sleep to academic performance and internalizing symptoms in large samples of young adults. Historically, studies that report both objectively measured sleep (e.g., actigraphy, polysomnography) and self-reported measures of sleep like questionnaires tend to show only modest relations between them (Girschik et al., 2012, Jackowska et al., 2016; Matthews et al., 2018; Trimmel et al., 2021) While self-reports of sleep habits may be useful tools, they do seem to be only rough estimates of a person’s sleep behaviors. Therefore, we were interested in measuring sleep behavior and sleep disturbances using both a common self-report measure and a wearable device.

1.1. Present study

The present study tests five preregistered hypotheses. Hypothesis 1 states that poor sleep outcomes (shorter average sleep duration, greater IIV in sleep duration, greater IIV in circadian midpoint, and lower average sleep efficiency) will be associated with more self-reported symptoms of depression and anxiety. Hypothesis 2 states that poor sleep outcomes will be associated with lower academic performance (i.e., lower cumulative GPA). Hypothesis 3 states that higher self-reported depressive and anxious symptoms will be associated with lower academic performance.

In addition to these straightforward bivariate relations, we also wanted to test various mechanistic pathways by which there could be relations between academic performance and sleep habits when considering a role for internalizing symptoms. Hypothesis 4 tests a mediation hypothesis, such that poor sleep outcomes will mediate the relation between academic performance and mental health. That is, a potential reason for the connection between poor academic performance and mental health could be that poor mental health causes, or also potentially a consequence of, poor sleep, which in turn would affect academic performance, for the reasons mentioned above. Hypothesis 5 tests a moderation hypothesis that sleep outcomes will moderate the relation between academic performance and mental health. This contrasts with the mediation hypothesis because rather than arguing that mental health would be a mechanistic explanation for the sleep-academic performance relation, sleep and mental health factors may have a combinatory effect on academic performance. That is, poor sleep outcomes could be an exacerbating factor in the relation between mental health and academic performance (i.e., stronger relation between academic performance and mental health with poor sleep metrics), whereas better sleep outcomes could serve as a protective factor (i.e., weaker relation between academic performance and mental health with good sleep metrics). To test these hypotheses, we examined sleep habits, measured objectively via wearable fitness trackers, academic performance, and self-reported aspects of mental health in a large sample of young adults at a comprehensive public university in the U.S.

2. Method

We report how we determined our sample size, all measures, and all exclusions, when necessary.

2.1. Transparency and openness

All data and analysis scripts can be found on the Open Science Framework (OSF). These data were collected as part of a broader multisession study examining individual differences in cognitive functioning, psychophysiology, personality, mental health, sleep habits, physical activity, and academic performance. After all data were collected, but before we conducted any analyses, we preregistered our hypotheses and data analysis plan on OSF: https://osf.io/m8gbp. Data and analysis code can also be found on OSF: https://osf.io/j4tzh/

2.2. Participants and procedure

The full sample for the broader study included 255 participants, all students at the University of Texas at Arlington (Mage = 19.02 years, SD = 1.81 years, range = [17, 34]; 217 participants identified as women, 58 as men, 2 as non-binary; 44% White, 24% Asian, 24% Black or African American, 10% other race, 2% Native American,1 35% Hispanic or Latino, 71% were native English speakers). The target sample size for the broader study was a minimum of 200 participants. Data collection stopped due to several factors including the end of an academic semester, the relocation of the lab to a different university, and the exhaustion of available funding for participant payments. The study comprised four laboratory visits each lasting about 2.5 hours. On the first visit, participants read and signed an informed consent document, completed a brief demographic survey, were administered a battery of computerized cognitive tasks, and were set up with a Fitbit Charge 5 device. Visit 2 comprised a battery of cognitive tasks. Visit 3 repeated the cognitive task battery from Visit 1. On Visit 4, the cognitive task battery from Visit 2 was repeated, and participants completed a set of self-report questionnaires (see details below). Visits were separated by one week. The cognitive and psychophysiological data will not be analyzed or reported upon here. Interested readers may consult Robison et al. (2026) for more information on these measures. The protocol was approved by the Institutional Review Board of the University of Texas at Arlington.

2.3. Sleep measures

Sleep data were recorded via the Fitbit Charge 5 activity bands for an average of 21 days. The device records sleep times, wake times, sleep duration, sleep efficiency (minutes asleep/minutes in bed), and number of nighttime awakenings. We used the daily logs to compute several variables, which are defined below.

2.3.1. Total sleep time

For each night, we used the total minutes asleep recorded by the Fitbit to obtain total sleep time. For each participant, we used these data to compute both a mean sleep duration and standard deviation in sleep duration. The within-person standard deviation was used as the measure of IIV in total sleep time.

2.3.2. Sleep efficiency

For each night, we computed sleep efficiency as the ratio of minutes asleep over minutes in bed. For each participant, we used these data to compute both mean sleep efficiency and a standard deviation in sleep efficiency. The within-person standard deviation was used as the measure of IIV in sleep efficiency.

2.3.3. Circadian midpoint

For each night, we computed the circadian midpoint as the time point halfway between sleep time and wake time. For each participant, we computed both mean circadian midpoint and standard deviation in circadian midpoint. The within-person standard deviation was used as the measure of IIV in circadian midpoint.

2.3.4. Self-reported sleep quality

We used the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989) to measure subjective (i.e., self-reported) sleep quality. Participants completed this questionnaire at the end of Visit 4. We used the scoring guide provided by Buysse et al. (1989) to compute a global score. Higher PSQI global scores indicate lower sleep quality.

2.4. Internalizing symptoms

2.4.1. Depression

Self-reported depressive symptoms were measured using the Center for Epidemiological Studies Depression (CES-D) scale (Radloff, 1977). There are 20 items, each providing a statement (e.g., “I was bothered by things that usually don’t bother me.”), which were answered based on how often the person experiences those thoughts in the previous week, on a 4-point scale (1 = rarely or none of the time, 2 = some of a little of the time, 3 = occasionally or a moderate amount of time, 4 = most of all of the time).

2.4.2. Trait anxiety

Trait anxiety was measured using the brief Spielberger Trait Anxiety Index (STAI; Marteau & Bekker, 1992). Participants reported both state and trait anxiety, but here we focused only on trait scores. The scale includes 6 items, three positive indicators of anxiety and three negative indicators of anxiety. The items asked participants to rate how well each of the adjectives described them, generally, on a 4-point scale (1 = not at all, 2 = somewhat, 3 = moderately so, 4 = very much so).

2.4.3. Academic stress

Self-reported academic stress was measured using the Perceptions of Academic Stress Scale (Bedewy & Gabriel, 2015). The scale asks participants to respond to 18 items (e.g., “Competition with my peers for grades is quite intense”) on a 5-point scale (1 = strongly disagree, 2 = somewhat disagree, 3 = neither agree nor disagree, 4 = somewhat agree, 5 = strongly agree).

2.5. Academic performance

During the informed consent process, participants were asked to provide an additional consent to release their academic records to the research team. If they consented, they signed and provided their university-administered student ID number.2 We then used these numbers to request academic records (semester-by-semester GPA, program of study, status, etc.) from the University of Texas at Arlington. We used cumulative GPA as our dependent variable in the analyses.

2.6. Analysis

The data were analyzed in R (R Core Team, 2024) using the tidyverse (Wickham et al., 2016), psych (Revelle, 2024), and lavaan (Rosseel, 2012) packages. Plots were generated using the ggplot2 (Wickham, 2016) and cowplot (Wilke, 2024) packages. Dates and times were analyzed using the lubridate (Grolemund & Wickham, 2011), chron (James & Hornik, 2023), and hms (Müller, 2023) packages. Analyses were reported using the effectsize (Ben-Shachar et al., 2020), papaja (Aust & Barth, 2024). To ensure reproducibility of our analysis script, we tagged each package with a version date of 11 November 2024, using the groundhog (Simonsohn & Gruson, 2025) package. Reliability for the Fitbit sleep measures was computed as a Spearman-Brown split-half corrected correlation between the odd- and even-numbered days of data collection. For the self-report scales, reliability was computed using McDonald’s ω, after reverse-scoring items as necessary.

2.7. Exclusions

To be included in the analyses for the sleep measures, participants’ Fitbit devices needed to provide at least seven nights’ worth of data. For all variables, we excluded anyone whose data fell outside ±3 standard deviations of the mean for that measure. Achieved sample sizes for all dependent measures are listed in Table 1. All available data were used to compute pairwise correlations.

Table 1.

Descriptive statistics.

Measure N Mean SD Skew Kurtosis Reliability
1. Total sleep time M 209 6.65 0.78 −0.19 −0.17 0.75
2. Sleep efficiency M 205 0.88 0.02 0.14 0.33 0.87
3. Circadian midpoint M 209 −2.63 1.56 0.29 −0.26 0.79
4. Total sleep time SD 209 1.45 0.43 0.32 −0.18 0.46
5. Sleep efficiency SD 208 0.03 0.01 1.04 1.51 0.63
6. Circadian midpoint SD 206 2.19 1.03 1.17 1.17 0.63
7. PSQI Global 249 6.60 2.53 0.36 −0.20 –
8. Depression 240 2.15 0.76 0.83 0.25 0.95
9. Trait anxiety 241 2.95 0.64 −0.23 −0.69 0.88
10. Academic stress 242 3.12 0.39 −0.36 0.08 0.86
11. Cumulative GPA 246 3.23 0.65 −0.86 0.01 –
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3. Results

Descriptive statistics are listed in Table 1, and correlations among the measures are listed in Table 2. According to the Fitbit devices, participants were asleep for an average of 6.65 hours per night and were in bed for an average of about 7.56 hours per night. According to their responses on the PSQI, participants’ average bed time was 12:38 AM and their average wake up time was 8:04 AM, which works out to about the same amount of time in bed as computed by the Fitbit (7.43 hours). The average cumulative GPA was 3.23. All variables were normally distributed, with |skew| and |kurtosis| < 2 for all measures. Most showed acceptable reliability, as well. One notable exception was IIV in total sleep time, which had a reliability estimate of .46. Although not part of our hypotheses, some relations among measures, and lack thereof, are worth noting. First, participants who had a later circadian midpoint tended to sleep less overall and have more variability in sleep time night-to-night. Second, people who self-reported poorer sleep quality (i.e., higher PSQI global scores) tended to have a later and more variable circadian midpoint and have more variability in total sleep time night-to-night. Third, the self-reported variables were all significantly inter-correlated. People who reported poorer sleep quality tended to self-report more depressive symptoms, higher trait anxiety, and higher academic stress; people who reported more depressive symptoms tended to report higher trait anxiety and academic stress; and people who reported higher trait anxiety tended to report more depressive symptoms.

Table 2.

Correlations among measures.

Measure 1. 2. 3. 4. 5. 6. 7. 8. 9 10.
1. Total sleep time M –
2. Sleep efficiency M .09 –
3. Circadian midpoint M −.64 .00 –
4. Total sleep time SD −.09 −.02 .26 –
5. Sleep efficiency SD −.10 .10 .08 .09 –
6. Circadian midpoint SD −.31 −.06 .46 .61 .03 –
7. PSQI Global −.12 −.01 .16 .21 .09 .21 –
8. Depression −.06 .04 .05 .10 .10 .11 .43 –
9. Trait anxiety −.08 .04 .07 .01 −.02 .09 .24 .52 –
10. Academic stress .00 .14 .01 .12 −.05 .07 .26 .32 .27 –
11. Cumulative GPA −.02 .09 .02 −.18 .02 −.11 −.16 −.06 −.01 .02
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Note: Bolded correlated are significant at p < .05.

Before testing the hypotheses, we inspected whether several covariates were related to GPA, such that they might need to be controlled for subsequent analysis. There was no relation between age and GPA, r (240) = −0.10, p = .11), nor a significant difference between men and women in GPA, Mmen = 3.24, SD = 0.51; Mwomen = 3.22, SD = 0.70; t(242) = 0.17, p = .86, d = −0.03 [−0.33, 0.27], nor a significant difference between native and non-native English speakers, Mnative = 3.20, SD = 0.66; Mnon-native = 3.28, SD = 0.64; t (247) = 0.87, p = .38, d = 0.12 [−0.15, 0.40]. Because none of the covariates were significantly associated with GPA, we use GPA without controlling for any covariates in the subsequent analyses.

The evidence for Hypothesis 1 was mixed, and it was supported only by self-reported sleep quality. Neither average nor intraindividual variability therein of total sleep time, sleep efficiency, nor circadian midpoint measured by the Fitbit significantly correlated with self-reported depression, anxiety, or academic stress (see Table 2). However, participants who self-reported lower sleep quality reported significantly more depression, trait anxiety, and academic stress.

Hypothesis 2 was mostly unsupported by the data. Of the variables measuring sleep quality, only intraindividual variability in total sleep time significantly correlated with cumulative GPA (see Table 2). However, this correlation was in the hypothesized direction. Self-reported lower sleep quality was also significantly correlated with cumulative GPA, such that people who reported more sleep disturbances on the PSQI tended to have lower GPAs.

There was no evidence in support of Hypothesis 3. There were non-significant, near-zero correlations between cumulative GPA and each of depression, trait anxiety, and academic stress (see Table 2).

We conducted the mediation analysis of the relation between sleep and academic performance via mental health (Hypothesis 4). To do so, we specified a set of three models in which relations between depression, anxiety, and academic stress, respectively, and GPA were mediated by the average total sleep time, variability in total sleep time, average circadian midpoint, variability in circadian midpoint, average sleep efficiency, and variability in sleep efficiency. As can be seen in Figure 1, the direct paths were all non-significant, and therefore so were all the indirect effects. Therefore, there was no evidence in favor of Hypothesis 4.

Figure 1.

Figure 1.

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Mediation analyses. Note: Numbers listed on paths are standardized regression coefficients with standard errors in parentheses. All paths were non-significant at α = .05.

Finally, to test Hypothesis 5, we specified separate linear models in which depression, anxiety, and academic stress were allowed to interact with each of average total sleep time variability in total sleep time, average circadian midpoint, variability in circadian midpoint, average sleep efficiency, and variability in sleep efficiency (18 total models). The models are summarized in Table 3. Because of the many models run for this hypothesis test, we corrected our α level for multiple comparisons to .002 (.05/18). None of the main effects or interaction terms for any model were significant at this threshold. Therefore, there was no evidence in favor of Hypothesis 5.

Table 3.

Moderation analyses.

Model Predictor B SE p
1 Total sleep time M −0.04 0.06 .62
Depression −0.07 0.06 .26
Total sleep time M x Depression 0.04 0.07 .57
2 Total sleep time SD −0.13 0.07 .05
Depression −0.05 0.07 .40
Total sleep time SD x Depression −0.02 0.07 .77
3 Circadian midpoint M 0.02 0.06 .78
Depression −0.08 0.07 .27
Circadian midpoint M x Depression −0.04 0.07 .53
4 Circadian midpoint SD −0.09 0.07 .21
Depression −0.07 0.07 .32
Circadian midpoint SD x Depression 0.06 0.07 .41
5 Sleep efficiency M 0.11 0.08 .19
Depression −0.07 0.07 .29
Sleep efficiency M x Depression 0.09 0.08 .29
6 Sleep efficiency SD 0.02 0.09 .82
Depression −0.08 0.07 .23
Sleep efficiency SD x Depression −0.02 0.1 .79
7 Total sleep time M −0.04 0.07 .51
Anxiety 0.001 0.06 .99
Total sleep time M x Anxiety 0.05 0.07 .44
8 Total sleep time SD −0.14 0.07 .03
Anxiety 0.01 0.06 .93
Total sleep time SD x Anxiety 0.02 0.06 .81
9 Circadian midpoint M 0.02 0.06 .75
Anxiety 0.01 0.06 .95
Circadian midpoint M x Anxiety −0.02 0.06 .79
10 Circadian midpoint SD −0.11 0.07 .17
Anxiety 0.01 0.06 .93
Circadian midpoint SD x Anxiety 0.03 0.07 .65
11 Sleep efficiency M 0.09 0.08 .25
Anxiety −0.004 0.06 .94
Sleep efficiency M x Anxiety −0.006 0.08 .94
12 Sleep efficiency SD −0.001 0.09 .99
Anxiety −0.01 0.06 .85
Sleep efficiency SD x Anxiety −0.09 0.08 .27
13 Total sleep time M −0.03 0.06 .60
Academic Stress 0.02 0.06 .77
Total sleep time M x Academic Stress 0.003 0.07 .97
14 Total sleep time SD −0.14 0.07 .03
Academic Stress 0.05 0.06 .47
Total sleep time SD x Academic Stress 0.09 0.06 .12
15 Circadian midpoint M 0.02 0.06 .75
Academic Stress 0.01 0.06 .87
Circadian midpoint M x Academic Stress 0.02 0.06 .74
16 Circadian midpoint SD −0.09 0.07 .21
Academic Stress 0.02 0.07 .72
Circadian midpoint SD x Academic Stress 0.04 0.07 .55
17 Sleep efficiency M 0.07 0.08 .40
Academic Stress −0.01 0.06 .93
Sleep efficiency M x Academic Stress −0.15 0.07 .03
18 Sleep efficiency SD 0.01 0.09 .92
Academic Stress 0.01 0.06 .82
Sleep efficiency SD x Academic Stress −0.03 0.09 .72
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Note: The dependent variable in every model was cumulative GPA. B = standardized regression coefficient, SE = standard error of the estimate.

3.1. Exploratory analyses of circadian disruptions

In all our analyses, we collapsed across weekends and weekdays. But potentially, important information can be gleaned by examining sleep discrepancies between weekends and weekdays, as some people may have been forced to adopt an earlier circadian rhythm than is natural to them because of the demands of the school/work day. Unsurprisingly, according to the measurements taken from the Fitbit, participants spent significantly more time in bed on weekends (M = 7.86 hours, SD = 1.34) than weekdays (M = 7.40, SD = 0.96; t[229] = 5.57, p < .001, dz = 0.37 [0.23, 0.50]), were asleep for longer on weekends (M = 6.95, SD = 1.18) compared to weekdays (M = 6.51, SD = 0.85; t[229] = 6.09, p < .001, dz = 0.40 [0.27, 0.54]), and had a later circadian midpoint on weekends (M = 5:25 AM, SD = 1.48 hours) compared to weekdays (M = 4:23 AM, SD = 1.19 hours; t[223] = 10.06, p < .001, dz = 0.67 [0.53, 0.72]. Thus, it is worth considering whether people whose preferred sleep schedule, which presumably would be reflected by weekend behavior, differs more from their weekday sleep schedule, had poorer outcomes. Correlations between the weekend/weekday time-in-bed, total sleep time, and circadian midpoint discrepancies and GPA, depression, anxiety, and academic stress are listed in Table 4. For the most part, these correlations were close to zero. Therefore, we did not find evidence that people with greater weekday/weekend discrepancies in their sleep behaviors had lower GPAs, nor did they report higher depression, higher anxiety, or more academic stress.

Table 4.

Correlations between weekday-to-weekend sleep changes and internalizing.

Measure 1. 2. 3. 4. 5. 6.
1. Δ Time in bed —
2. Δ Total sleep time .99 —
3. Δ Circadian midpoint .41 .42 —
4. Cumulative GPA .03 .02 −.01 —
5. Depression .04 .04 .06 −.06 —
6. Anxiety .17 .17 .12 −.01 .52 —
7. Academic Stress −.10 −.11 −.05 .02 .32 .27
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Note: Δ = difference between weekend and weekday measures. Positive values mean more time in bed, total sleep time, or later circadian midpoint on weekends (Friday and Saturday nights) compared to weekdays. Bolded correlations are significant at p < .05.

4. Discussion

The present study investigated relations among sleep, internalizing symptoms, and academic performance in a sample of college students. We measured sleep during a 3-week interval using wrist-worn actigraphy bands. Internalizing symptoms were self-reported by participants at the conclusion of the study, and with student consent, GPAs were obtained via the university’s registrar. Although exact mechanisms behind the correlation between internalizing symptoms and academic performance are still under investigation, several explanations have been offered to understand the possible causal relationship between the two. For one, the impairment of cognitive processes, including memory, attention, and executive function are well-established consequences of depression, anxiety, and emotional distress (Khan et al., 2024). Further, the psychological outcomes of these symptoms may contribute to the low achievement outcomes. Depression is characterized by negative thinking, lethargy, disinterest, and negative emotions, all of which may impact academic performance on assignments and exams due to reduced motivation (Seun-Fadipe & Mosaku, 2017). Anxiety and stress are similarly attributed to deficits in cognitive performance as a result of psychological distress: hopelessness, negative and distracted moods and low motivation are all significant inhibitors of performance (Lisnyj et al., 2020).

Based on this prior theorizing, we hypothesized that elevated internalizing symptoms (e.g., higher depression and higher anxiety) would be associated with poorer sleep (e.g., less total sleep time, more variability in total sleep time, less sleep efficiency). Although participants who self-reported more sleep disruptions did report more depressive symptoms and higher trait anxiety, there were near-zero and non-significant correlations between the actigraphy-based sleep measures and internalizing symptoms. Our second hypothesis was that participants with higher internalizing symptoms would tend to have lower GPAs. This hypothesis was also largely unsupported. There was only one significant correlation between a sleep metric and GPA. People who had more night-to-night variable in total sleep time tended to have lower GPAs (r = −.18). Finally, we hypothesized that more internalizing symptoms would be associated with lower GPAs. This hypothesis was also unsupported.

A few findings from our study are worth noting, despite the lack of support for most of our hypotheses. First, participants in the study only spent about 7–8 hours in bed per night resulting in about 6–7 hours of sleep per night. While this figure seems low, it is exactly on par with what Creswell et al. (2023) estimated as the average nightly sleep duration (6.62 hours) in their samples of first-year undergraduates at three different universities. A panel of experts from the National Sleep Foundation has recommended young adults (age 18 – 25) sleep 7 to 9 hours per night (Hirshkowitz et al., 2015). Therefore, the students in our sample were mostly not sleeping enough according to these guidelines. That said, it is not uncommon for young adults, particularly college students, to keep suboptimal sleep schedules. Becker et al. (2018) found that a majority (~62%) of college students meet criteria for “poor sleep,” according to their responses to the PSQI, and only 37% reporting that they slept more than 7 hours per night, on average. Similarly, Lund et al. (2010) found that 60% of college student respondents to the PSQI met criteria for poor sleep, as well. So, despite our sample exhibiting suboptimal sleep behaviors, on average, the sample does seem to exhibit behavior that is representative of college students.

4.1. Limitations

There are several potential reasons for the largely null correlations among sleep, internalizing symptoms, and academic performance. The first is that the relations are indeed largely null, or weak. A meta-analysis published around the time this study was initiated found that among adolescents, only negligible correlations between sleep duration academic performance and between sleep quality and academic performance have been observed, on average (Musshafen et al., 2021). Although the present study focused on young adults, rather than adolescents, perhaps the relations are similarly weak in secondary and post-secondary students. As a recommendation, Musshafen et al. (2021) suggest that future work focus on objective measures of sleep duration/quality and academic performance, as much prior work had relied on self-report. The present study attempted to objectively measure both sleep and academic performance. Further, a meta-analysis of relations between psychological factors and undergraduate academic performance found only small average correlations of GPA with depression (r = −.10) and with academic stress (r = −.12; Richardson et al., 2012). Thus, it may be that these relations are indeed weak and only inconsistently observed.

A second reason for the null relations might be measurement issues. While the average sleep metrics had acceptable split-half reliabilities, reliabilities for the IIV metrics were considerably lower. This was due, in part, because of the 21-day sampling period. With more samples, it is likely these estimates would have stabilized. It was not possible to measure sleep for a longer period, since our study was scheduled for lab visits on 4 consecutive weeks. Thus, it is possible that more samples within people would stabilize estimates in terms of reliability. It is worth noting that some studies have indeed found correlations between IIV measures and psychological outcomes, even with shorter sampling periods (Bei et al., 2017; Gao & Scullin, 2024, Mezick et al., 2009; Slavish et al., 2019; Veeramachaneni et al., 2019). Further, all the objective measures of sleep depend on the degree to which the algorithms that the Fitbit devices use to track sleep are accurate. For example, the “minutes in bed” measure used to compute sleep efficiency is calculated using sleep onset and offset, using a combination of heart rate metrics and motion. It may be an underestimate for people with sleep disturbances, and therefore we may end up overestimating sleep efficiency for those people. In future work, pairing sleep diaries with Fitbit measurements and with the PSQI may help triangulate objective sleep metrics.

While prior work has shown that disruptions to circadian rhythms can affect people both physically and psychologically (see Walker et al., 2020 for review), we did not find that people with more weekend/weekday sleep discrepancies, which would presumably reflect a tendency to desire a different sleep/wake times than their weekday schedule permits, had lower GPAs, higher depression or anxiety, or higher academic stress. However, we did not include a measure of circadian phase preference in our design.

Finally, the data were collected from a non-clinical sample. Although people were not excluded from the study if they had clinically significant symptoms of depression, anxiety, or any other internalizing disorder. Nor were people excluded if they reported clinically significant levels of sleep disruption. However, the relations among the variables observed here may be more pronounced among individuals diagnosed with disordered internalizing, disordered sleep, or both. Further, we did not have a clinical assessment of internalizing symptoms. So, the same problems that may threaten the validity of self-reported sleep behaviors may have also affected the depression and anxiety self-report scales, as well.

4.2. Conclusions

In a sample of college students at a comprehensive public university in the U.S., we did not observe consistent relations among objective measures of sleep duration, consistency, and efficiency, internalizing symptoms (i.e., anxiety and depression), and academic performance. Future work will be necessary to determine whether these relations are more pronounced in different developmental strata, within individuals with clinically significant symptomology, or with denser longitudinal sampling of these variables.

Acknowledgments

We would like to thank Niitia Davis, Miguel Ruiz, Jessica Hernandez, Doris Granados, Maddie Marsh, Connor Dupre, Jordan Nguyen, Alexandra Nieto, Sarah Cole, Claudio Rodriguez, Reyes Flores, Benjamin Rudd, Justine Lumbre, and Vu Ho for their assistance with data collection.

Funding

This research was supported by a grant from the U.S. Naval Research Laboratory [N00173-22-2-C006] awarded to M. K. R and by a grant from the National Institutes of Health [K23HL14158] awarded to I. V.

Footnotes

Disclosure statement

No potential conflict of interest was reported by the author(s).

1

Participants were allowed to mark as many racial categories as applied.

2

The vast majority (97%) of participants consented to release their academic records during this process. Therefore we were not concerned with a willingness to disclose biasing this measure.

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