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Sleep and Circadian Predictors of Academic Performance and Retention in STEM Pathways: A Longitudinal Study in University Freshmen

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Title: Sleep and Circadian Predictors of Academic Performance and Retention in STEM Pathways: A Longitudinal Study in University Freshmen
Language: English
Authors: Corinne L. Fitzsimmons (ORCID 0009-0001-4911-256X), Jason R. Carter (ORCID 0000-0001-7350-6537), Michael K. Scullin (ORCID 0000-0002-7578-7587)
Source: Advances in Physiology Education. 2026 50(2):394-401.
Availability: American Physiological Society. 9650 Rockville Pike, Bethesda, MD 20814-3991. Tel: 301-634-7164; Fax: 301-634-7241; e-mail: webmaster@the-aps.org; Web site: https://www.physiology.org/journal/advances
Peer Reviewed: Y
Page Count: 8
Publication Date: 2026
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 1920730
1943323
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Sleep, Predictor Variables, Academic Achievement, STEM Education, College Freshmen, Grade Point Average, Academic Persistence, Majors (Students)
Geographic Terms: Texas
Assessment and Survey Identifiers: Center for Epidemiologic Studies Depression Scale, State Trait Anxiety Inventory, Raven Advanced Progressive Matrices
DOI: 10.1152/advan.00313.2025
ISSN: 1043-4046
1522-1229
Abstract: Poor sleep health is common among university students, but there are diverging viewpoints on whether their sleep loss helps, harms, or has no impact on academic performance. We investigated whether sleep health markers in first-year university students predicted longitudinal academic outcomes when accounting for key variables. First-year university students who were pursuing a science, technology, engineering, and mathematics (STEM) career pathway (n = 489) were recruited to complete a baseline session that included measures of global sleep quality, chronotype, daytime sleepiness, social jetlag (change in sleep timing from weekdays to weekends), demographics, mental health, and fluid intelligence (reasoning). At the end of year 1 and year 2, we extracted data on cumulative grade-point average (GPA), academic major change, STEM pathway change, and institutional withdrawal. After adjusting for demographic, mental health, and fluid intelligence factors, we observed that worse global sleep quality, evening chronotype, and worse social jetlag independently predicted year 1 GPA. Global sleep quality also predicted year 2 GPA, even when accounting for prior academic performance. Students with shorter sleep durations were more likely to switch from their STEM career pathway, even when accounting for academic performance, demographics, mental health, and fluid intelligence. In conclusion, sleep health markers are predictive of better future academic performance and retention in STEM pathways. There is a need for individual and environmental interventions to improve sleep health in first-year students and to determine causal direction.
Abstractor: As Provided
Notes: https://osf.io/jmavn
Entry Date: 2026
Accession Number: EJ1502785
Database: ERIC
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  Value: <anid>AN0194748791;apu01jun.26;2026Jun24.01:16;v2.2.500</anid> <title id="AN0194748791-1">Sleep and circadian predictors of academic performance and retention in STEM pathways: a longitudinal study in university freshmen </title> <sbt id="AN0194748791-2">INTRODUCTION</sbt> <p>Poor sleep health is common among university students, but there are diverging viewpoints on whether their sleep loss helps, harms, or has no impact on academic performance. We investigated whether sleep health markers in first-year university students predicted longitudinal academic outcomes when accounting for key variables. First-year university students who were pursuing a science, technology, engineering, and mathematics (STEM) career pathway (n = 489) were recruited to complete a baseline session that included measures of global sleep quality, chronotype, daytime sleepiness, social jetlag (change in sleep timing from weekdays to weekends), demographics, mental health, and fluid intelligence (reasoning). At the end of year 1 and year 2, we extracted data on cumulative grade-point average (GPA), academic major change, STEM pathway change, and institutional withdrawal. After adjusting for demographic, mental health, and fluid intelligence factors, we observed that worse global sleep quality, evening chronotype, and worse social jetlag independently predicted year 1 GPA. Global sleep quality also predicted year 2 GPA, even when accounting for prior academic performance. Students with shorter sleep durations were more likely to switch from their STEM career pathway, even when accounting for academic performance, demographics, mental health, and fluid intelligence. In conclusion, sleep health markers are predictive of better future academic performance and retention in STEM pathways. There is a need for individual and environmental interventions to improve sleep health in first-year students and to determine causal direction. NEW & NOTEWORTHY Many believe that science, technology, engineering, and mathematics (STEM) students must sacrifice their sleep to achieve academic success. In striking contrast, the current research demonstrates that better sleep health in freshmen STEM majors predicted better academic success, even after accounting for mental health, fluid intelligence, and prior academic performance. The current work was the first to document that sleep health predicted better persistence in a STEM track across 2 yr.</p> <p>Poor sleep is common in university students ([<reflink idref="bib1" id="ref1">1</reflink>]). Research indicates that first-year university students often need 8 h/night of sleep ([<reflink idref="bib3" id="ref2">3</reflink>]), but many students habitually sleep fewer than 7 h on weekday nights ([<reflink idref="bib4" id="ref3">4</reflink>]). Poor sleep quality in this population can be driven by academic pressures, delayed circadian timing, mental health conditions, and early class start times ([<reflink idref="bib6" id="ref4">6</reflink>]), but sleep-restricting behaviors are also common. Average bedtimes are approximately 2 AM ([<reflink idref="bib8" id="ref5">8</reflink>]), and ∼35% of students stay up until 3 AM at least once a week ([<reflink idref="bib1" id="ref6">1</reflink>]). The majority of first-year undergraduates report pulling at least one "all-nighter" ([<reflink idref="bib10" id="ref7">10</reflink>]), and 20% of students report engaging in this practice within the last month ([<reflink idref="bib1" id="ref8">1</reflink>]). Sleep-restricting habits are often the worst among students in the most academically demanding majors ([<reflink idref="bib11" id="ref9">11</reflink>]).Sleep deprivation is consequential to health and functioning ([<reflink idref="bib13" id="ref10">13</reflink>]). Laboratory studies indicate that sleep is important to attention, working memory, and memory consolidation ([<reflink idref="bib14" id="ref11">14</reflink>]). As each of these cognitive processes is relevant to learning ([<reflink idref="bib17" id="ref12">17</reflink>]), poor sleep health has been posited to predict deficiencies in academic outcomes ([<reflink idref="bib18" id="ref13">18</reflink>]). However, it is common to hear from students that they sacrifice sleep to improve their academic outcomes. While these reports may reflect students' misperceptions of their cognitive capacity when sleep deprived ([<reflink idref="bib19" id="ref14">19</reflink>]), some empirical data suggest caution in dismissing these reports ([<reflink idref="bib20" id="ref15">20</reflink>]). For example, not all cognitive functions decline with sleep restriction; the ability to reason through and solve novel problems (fluid intelligence) ([<reflink idref="bib21" id="ref16">21</reflink>]) typically remains stable following mild sleep loss ([<reflink idref="bib15" id="ref17">15</reflink>]). In addition, some individuals are more resilient to sleep loss than others ([<reflink idref="bib22" id="ref18">22</reflink>]), and there is evidence that reducing sleep duration can be temporarily adaptive in some settings ([<reflink idref="bib23" id="ref19">23</reflink>]), raising the possibility that staying up late to study could possibly help achieve higher grades at the cost of less sleep. As such, it is not clear whether better sleep health would help, harm, or have no impact on academic performance ([<reflink idref="bib20" id="ref20">20</reflink>], [<reflink idref="bib24" id="ref21">24</reflink>], [<reflink idref="bib25" id="ref22">25</reflink>]).There are a few highly cited papers reporting that students with better sleep health show better academic performance (for review, see Ref. [<reflink idref="bib26" id="ref23">26</reflink>]), but a broader inspection of the literature suggests equivocal outcomes. For example, in a systematic review, only half of the studies showed a positive relationship between sleep measures and academic achievement ([<reflink idref="bib27" id="ref24">27</reflink>]), and a meta-analysis indicated that the associations were negligible overall ([<reflink idref="bib28" id="ref25">28</reflink>]). The bulk of this literature though has been limited by using cross-sectional designs (>80% of studies), relying on self-reported academic outcomes (>36% of studies), and lacking control for covariates (>60% of studies; Ref. [<reflink idref="bib27" id="ref26">27</reflink>]) such as prior academic performance, demographic variables, general intelligence, and mental health factors, which are independently associated with academic performance ([<reflink idref="bib29" id="ref27">29</reflink>]). In addition, nearly all prior studies examined cumulative grade-point average (GPA) as the sole academic outcome ([<reflink idref="bib32" id="ref28">32</reflink>]), but this metric is only one potential marker of academic success over time (i.e., career shift, institutional retention, social mobility, etc.). Furthermore, most studies have sampled student groups broadly across both disciplines and years in undergraduate/graduate studies, yet the first year of college is arguably the most complex and critical developmental transition point for establishing healthy sleep patterns and effective studying habits in university students ([<reflink idref="bib33" id="ref29">33</reflink>]).The current work investigated whether sleep health in first-year university students was predictive of longitudinal academic outcomes. We had three goals. First, we investigated whether five components of sleep health, sleep quality, sleep duration, circadian preference, sleepiness, and social jetlag (i.e., weekday to weekend shifting of midpoints in sleep), predicted official GPAs in year 1 and year 2 after accounting for factors known to impact academic performance (i.e., demographics, mental health, and fluid intelligence factors). Second, GPAs were the primary outcome variables, but we also investigated changes in academic major, switching away from the field of science, technology, engineering, and mathematics (STEM), and withdrawing from the institution. Third, based on conceptual and empirical work on vulnerability/resilience to sleep loss, and specifically work that concluded that the strength of sleep-cognition associations may depend on sex, race/ethnicity group identification, intelligence, or mental health factors ([<reflink idref="bib34" id="ref30">34</reflink>]), the significant outcomes were further tested for moderation.Based on mechanistic work on sleep and cognition, we hypothesized that higher sleep quality, longer total sleep time (TST), a morning circadian preference, lower social jetlag, and lower levels of daytime sleepiness would predict better academic outcomes (i.e., higher GPAs and retention) ([<reflink idref="bib17" id="ref31">17</reflink>]). Alternatively, based on the perspective that people adapt their amount of sleep to their environment to maximize performance outcomes, sleep health markers could be unrelated, or even negatively predictive, of academic outcomes ([<reflink idref="bib20" id="ref32">20</reflink>], [<reflink idref="bib24" id="ref33">24</reflink>]).</p> <hd id="AN0194748791-3">MATERIALS AND METHODS</hd> <p>First-year undergraduate students (<emph>n</emph> = 499) at Baylor University were recruited between 2019 and 2023. The study sample was designed to detect small-medium sized associations (<emph>r</emph> = 0.15) with >0.90 power. Inclusion criteria consisted of being a first-year undergraduate student with intentions to pursue a STEM major or STEM career path. STEM major/career direction was operationally defined as majoring in astronomy, astrophysics, aviation sciences, biochemistry, bioinformatics, biology, chemistry, computer science, earth science, engineering, environmental health science, geology, geophysics, health sciences, mathematics, neuroscience, physics, science research fellows, or statistics. Students with premedical, predental, preveterinary, preoptometry, and prephysician assistant classifications were also eligible to participate.Participants completed a battery of questionnaires and cognitive assessments lasting up to 4 h. This report focused on the data from the sleep questionnaires, mental health scales, a fluid intelligence task, and longitudinal academic outcomes, which are described below. The study was approved by the Institutional Review Board of Baylor University, and all individuals provided written informed consent (for students <18 yr old, parental consent was obtained with student assent). Upon study completion, participants were debriefed and received monetary compensation.</p> <hd id="AN0194748791-4">Measures</hd> <p>Demographic variables of interest included self-reported age, sex, and race/ethnicity group identification. Following prior work on sleep disparities in university students ([<reflink idref="bib4" id="ref34">4</reflink>]), race/ethnicity identifications were further binarily coded for black, indigenous, and other people of color (BIPOC) versus non-BIPOC. Standardized questionnaires are detailed below.</p> <hd id="AN0194748791-5">Pittsburgh Sleep Quality Index</hd> <p>Pittsburgh Sleep Quality Index (PSQI) is a 19-item self-report measure for assessing subjective sleep quality over the last month ([<reflink idref="bib39" id="ref35">39</reflink>]). This measure produces a global score where higher scores indicate worse global sleep quality and global scores ≥6 are considered indicative of clinically poor sleep quality.</p> <hd id="AN0194748791-6">Social Jetlag</hd> <p>Social jetlag is the difference in sleep schedules from weekdays to weekends, which can cause circadian misalignment. Participants reported their typical bedtimes, wake times, and sleep durations, separated by weekdays and weekends. We used these values to calculate the midpoint (i.e., time of day halfway into sleep) of sleep on weekdays versus weekends using the correction recommendations from Roenneberg and colleagues ([<reflink idref="bib40" id="ref36">40</reflink>]). Absolute values of the differences were used to reduce the risk of obscuring results related to the disruption of circadian alignment, as directional preference is already captured with the Morningness-Eveningness Questionnaire (MEQ) discussed below. Weekday sleep duration was also extracted alone as a separate independent variable ([<reflink idref="bib4" id="ref37">4</reflink>]).</p> <hd id="AN0194748791-7">Epworth Sleepiness Scale</hd> <p>The Epworth Sleepiness Scale (ESS) is a commonly used measure in sleep medicine to assess the trait-level subjective sleepiness of an individual ([<reflink idref="bib41" id="ref38">41</reflink>]). In the ESS, participants rated the likelihood that they would doze in eight scenarios such as sitting as a passenger in a car. Responses ranged from 0 (would never doze) to 3 (high chance of dozing). Responses were then summed, with higher scores indicating greater daytime sleepiness (possible range: 0 to 24). Scores ≥10 are indicative of excessive daytime sleepiness.</p> <hd id="AN0194748791-8">Morningness-Eveningness Questionnaire</hd> <p>The 19-item MEQ was used to assess circadian preference (hereafter "chronotype"; Ref. [<reflink idref="bib42" id="ref39">42</reflink>]). Items included rating one's preferred times for rising, resting, activities, and energy levels at specific times of day (e.g., If you got into bed at 11:00 PM, how tired would you be?). Scores can range from 16 to 86 with higher scores showing a greater morning preference.</p> <hd id="AN0194748791-9">Center for Epidemiological Studies-Depression</hd> <p>The Center for Epidemiological Studies-Depression (CES-D) survey was used as a measure of depressive symptoms ([<reflink idref="bib43" id="ref40">43</reflink>]). Participants rated 20 items such as how often they felt sad during the last week. Item responses ranged from 0 (i.e., rarely or none of the time/less than 1 day) to 3 (i.e., most or all of the time/5 to 7 days). Positively worded items were reverse scored, and all items were then summed. The total possible range was 0 to 60, with higher scores indicating more depressive symptoms.</p> <hd id="AN0194748791-10">State Trait Anxiety Inventory Part II</hd> <p>State Trait Anxiety Inventory Part II (STAI II) assesses trait anxiety across 20 statements (e.g., "I feel calm"). The participant rated their level of agreement for each statement from 1 (not at all) to 4 (very much so). Reliability in college students is good (α =.90; Ref. [<reflink idref="bib44" id="ref41">44</reflink>]). Positively worded statements were reverse scored, and responses were summed to create a trait anxiety score. Scores could range from 20 to 80, with higher scores indicating more anxiety symptoms.</p> <hd id="AN0194748791-11">Raven's Advanced Progressive Matrices</hd> <p>Fluid intelligence was estimated based on Raven's Advanced Progressive Matrices (RAPM) performance. Participants viewed a 3 × 3 matrix of overlapping lines/shapes with one component missing. They were instructed to identify which pattern, out of eight options, completed the matrix. Participants were permitted 15 min to complete up to 36 problems. The number of correct responses was used as the RAPM score ([<reflink idref="bib45" id="ref42">45</reflink>]).</p> <hd id="AN0194748791-12">Grade-Point Averages</hd> <p>Undergraduate cumulative GPA in the United States is typically calculated on a 0.0–4.0 scale with higher scores indicating better performance and grades falling below 2.0 considered failing. The cumulative grade averages the score for each class, weighted by the course's hours. The cumulative GPA was extracted from university records for the end of the respective school year (spring semester). Those who were not enrolled at the end of the year were not included in this analysis.</p> <hd id="AN0194748791-13">Academic Major/Track and Institutional Retention</hd> <p>Switching academic majors and switching out of a STEM major/track were determined by comparing students' reported academic major/track at baseline to their university records at the end of their first year and second year (which included information on history of changes in major and changes in premedical/prehealth track status). Students who were not enrolled at the end of the respective year and did not show enrollment in later semesters were counted as institutional withdrawals.</p> <hd id="AN0194748791-14">Prior Academic Ability</hd> <p>A subset of participants (71%) provided data on prior academic ability, operationalized as self-reported Scholastic Assessment Test (SAT) and American College Testing (ACT) scores, which are known predictors of first-year college GPA ([<reflink idref="bib16" id="ref43">16</reflink>]). For consistency, SAT scores were converted to ACT scores using the tables provided by College Board and ACT, Inc. ([<reflink idref="bib46" id="ref44">46</reflink>]).</p> <hd id="AN0194748791-15">Statistical Analysis</hd> <p>Data were initially screened for impossible values and corrected when possible (e.g., sleep timing was corrected from 2 AM to 11 PM to 2 AM to 11 AM when the individual reported 9 h of sleep duration). If the impossible value remained ambiguous for multiple raters, it was dropped (e.g., 144 h/night average sleep duration). Using Statistical Package for the Social Sciences version 29, we conducted a series of two-step regressions for each sleep measure to determine its relationship to each academic outcome (GPA, major change, STEM retention, and institutional withdrawal) for the first year and second year. Model 1 was adjusted for demographic variables (age, sex, and BIPOC status). Model 2 was adjusted for demographics, depression, anxiety, and fluid intelligence. For analyses of the second-year outcomes, first-year cumulative GPA was also included as a covariate in models 1 and 2. When significant sleep-academic associations were identified, we used the PROCESS v5 model to test for moderation by sex, BIPOC status, CES-D, STAI-II, and RAPM (components were mean centered, and bootstrapping was set to 5000). Alpha was set to 0.05. De-identified data and study materials are available at the Open Science Framework (https://osf.io/jmavn/).</p> <hd id="AN0194748791-16">RESULTS</hd> <p>Of the total enrolled sample (<emph>n</emph> = 499), we analyzed sleep and academic data from 489 participants who were in their first year of postsecondary education (i.e., excluding sophomores and transfer students; mean age = 18.27 yr, 72.2% females, and 53.8% BIPOC). By the end of year 2, 27% of the initial sample had changed their academic major, 10% had switched away from STEM majors and/or prehealth tracks, and 9.2% had withdrawn from the institution. Additional summary statistics for academic outcomes, sleep measures, and demographic variables are available in Table 1. Unadjusted correlations between sleep and academic measures are reported in Supplemental Table S1.</p> <p></p> <p>Open in Viewer</p> <p>Table 1. Descriptive statistics</p> <p> <ephtml> <table><thead><tr><th>Variable</th><th>Total <italic>n</italic></th><th>Mean (SD) or Percentage (<italic>n</italic>)</th></tr></thead><tbody><tr><td><p>Age</p></td><td><p>489</p></td><td><p>18.27 (0.56)</p></td></tr><tr><td><p>Sex (%female)</p></td><td><p>489</p></td><td><p>72.2% (353)</p></td></tr><tr><td><p>Race/ethnicity</p></td><td><p>489</p></td><td /></tr><tr><td><p> American Indian or Alaskan Native</p></td><td /><td><p>0.2% (1)</p></td></tr><tr><td><p> Asian or Pacific Islander</p></td><td /><td><p>21.9% (107)</p></td></tr><tr><td><p> Black or African American</p></td><td /><td><p>7.0% (34)</p></td></tr><tr><td><p> Hispanic or Latinx</p></td><td /><td><p>19.8% (97)</p></td></tr><tr><td><p> White</p></td><td /><td><p>46.2% (226)</p></td></tr><tr><td><p> Other</p></td><td /><td><p>1.4% (7)</p></td></tr><tr><td><p> Multiple</p></td><td /><td><p>2.7% (13)</p></td></tr><tr><td><p> Chose to not answer</p></td><td /><td><p>0.8% (4)</p></td></tr><tr><td><p>BIPOC</p></td><td><p>489</p></td><td><p>53.8% (263)</p></td></tr><tr><td><p>CES-D</p></td><td><p>481</p></td><td><p>18.89 (10.02)</p></td></tr><tr><td><p>STAI-II</p></td><td><p>472</p></td><td><p>44.78 (11.10)</p></td></tr><tr><td><p>RAPM</p></td><td><p>489</p></td><td><p>19.38 (4.30)</p></td></tr><tr><td><p>PSQI</p></td><td><p>483</p></td><td><p>5.76 (2.72)</p></td></tr><tr><td><p>Social jetlag</p></td><td><p>483</p></td><td><p>59.31 (44.14)</p></td></tr><tr><td><p>Weekday TST</p></td><td><p>487</p></td><td><p>418.39 (69.17)</p></td></tr><tr><td><p>ESS</p></td><td><p>486</p></td><td><p>9.06 (3.85)</p></td></tr><tr><td><p>MEQ</p></td><td><p>478</p></td><td><p>47.79 (9.35)</p></td></tr><tr><td><p>ACT (or SAT equivalent)</p></td><td><p>347</p></td><td><p>29.71 (3.78)</p></td></tr><tr><td><p>GPA: year 1</p></td><td><p>479</p></td><td><p>3.31 (0.60)</p></td></tr><tr><td><p>GPA: year 2</p></td><td><p>441</p></td><td><p>3.37 (0.49)</p></td></tr><tr><td><p>Academic major changed by end of year 1</p></td><td><p>489</p></td><td><p>11.9% (58)</p></td></tr><tr><td><p>Academic major changed by end of year 2</p></td><td><p>489</p></td><td><p>27% (132)</p></td></tr><tr><td><p>Switched out of STEM by end of year 1</p></td><td><p>489</p></td><td><p>5.7% (28)</p></td></tr><tr><td><p>Switched out of STEM by end of year 2</p></td><td><p>486</p></td><td><p>10% (49)</p></td></tr><tr><td><p>Institutional withdrawal in year 1</p></td><td><p>489</p></td><td><p>2% (10)</p></td></tr><tr><td><p>Institutional withdrawal in year 2</p></td><td><p>486</p></td><td><p>9.2% (45)</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>n</emph> = number of subjects. BIPOC, black, indigenous, and other people of color; CES-D, Center for Epidemiological Studies-Depression; ESS, Epworth Sleepiness Scale; GPA, grade-point-average; MEQ, Morningness-Eveningness Questionnaire; PSQI, Pittsburgh Sleep Quality Index; RAPM, Raven's Advanced Progressive Matrices; STAI II, State-Trait Anxiety Inventory Part II; Weekday TST, weekday total sleep time.</p> <p>In demographically adjusted models (model 1), better PSQI global sleep quality, lower social jetlag, greater weekday total sleep time (TST), and MEQ-defined morningness were predictive of higher first-year GPAs (Table 2). After additional adjustment for mental health and fluid intelligence (model 2), PSQI, social jetlag, and MEQ scores remained robust predictors of GPAs. For second-year GPAs, better PSQI global sleep quality was the sole predictor; this effect was robust, remaining significant even after controlling for first-year GPA and the other model 2 covariates (Table 2).</p> <p></p> <p>Open in Viewer</p> <p>Table 2. Standardized beta for sleep factors predicting cumulative GPA for the end of the first year and second year</p> <p> <ephtml> <table><thead><tr><td /><th colspan="4">Model 1<sup>a</sup></th><th colspan="4">Model 2<sup>b</sup></th></tr><tr><td /><th>β</th><th>SE</th><th><italic>t</italic></th><th><italic>P</italic></th><th>β</th><th>SE</th><th><italic>t</italic></th><th><italic>P</italic></th></tr></thead><tbody><tr><td><p>Year 1</p></td></tr><tr><td><p> PSQI global</p></td><td><p>−0.338</p></td><td><p>0.010</p></td><td><p>−7.66</p></td><td><p><bold><0.001</bold></p></td><td><p>−0.271</p></td><td><p>0.011</p></td><td><p>−5.27</p></td><td><p><bold><0.001</bold></p></td></tr><tr><td><p> Social jetlag</p></td><td><p>−0.146</p></td><td><p>0.001</p></td><td><p>−3.13</p></td><td><p><bold>0.002</bold></p></td><td><p>−0.128</p></td><td><p>0.001</p></td><td><p>−2.83</p></td><td><p><bold>0.005</bold></p></td></tr><tr><td><p> Weekday TST</p></td><td><p>0.154</p></td><td><p>0.000</p></td><td><p>3.32</p></td><td><p><bold><0.001</bold></p></td><td><p>0.090</p></td><td><p>0.000</p></td><td><p>1.93</p></td><td><p>0.055</p></td></tr><tr><td><p> ESS</p></td><td><p>−0.056</p></td><td><p>0.007</p></td><td><p>−1.19</p></td><td><p>0.236</p></td><td><p>−0.024</p></td><td><p>0.007</p></td><td><p>−0.53</p></td><td><p>0.595</p></td></tr><tr><td><p> MEQ</p></td><td><p>0.126</p></td><td><p>0.003</p></td><td><p>2.68</p></td><td><p><bold>0.008</bold></p></td><td><p>0.093</p></td><td><p>0.003</p></td><td><p>2.00</p></td><td><p><bold>0.046</bold></p></td></tr><tr><td><p>Year 2<sup>c</sup></p></td></tr><tr><td><p> PSQI global</p></td><td><p>−0.046</p></td><td><p>0.004</p></td><td><p>−2.02</p></td><td><p><bold>0.045</bold></p></td><td><p>−0.069</p></td><td><p>0.005</p></td><td><p>−2.70</p></td><td><p><bold>0.007</bold></p></td></tr><tr><td><p> Social jetlag</p></td><td><p>0.000</p></td><td><p>0.000</p></td><td><p>−0.01</p></td><td><p>0.995</p></td><td><p>−0.001</p></td><td><p>0.000</p></td><td><p>−0.05</p></td><td><p>0.960</p></td></tr><tr><td><p> Weekday TST</p></td><td><p>0.036</p></td><td><p>0.000</p></td><td><p>0.70</p></td><td><p>0.485</p></td><td><p>0.044</p></td><td><p>0.000</p></td><td><p>1.911</p></td><td><p>0.057</p></td></tr><tr><td><p> ESS</p></td><td><p>0.017</p></td><td><p>0.003</p></td><td><p>0.79</p></td><td><p>0.432</p></td><td><p>0.015</p></td><td><p>0.003</p></td><td><p>0.67</p></td><td><p>0.500</p></td></tr><tr><td><p> MEQ</p></td><td><p>0.026</p></td><td><p>0.001</p></td><td><p>1.18</p></td><td><p>0.239</p></td><td><p>0.028</p></td><td><p>0.001</p></td><td><p>1.23</p></td><td><p>0.219</p></td></tr></tbody></table> </ephtml> </p> <p>ESS, Epworth Sleepiness Scale; GPA, grade-point-average; MEQ, Morningness-Eveningness Questionnaire; PSQI, Pittsburgh Sleep Quality Index; Weekday TST, weekday total sleep time. <sups>a</sups>Adjusted for age, sex, and black indigenous people of color (BIPOC) status; <sups>b</sups>adjusted for age, sex, BIPOC status, depressive symptoms, trait anxiety, and fluid intelligence; <sups>c</sups>additionally adjusted for first-year GPA. Bolded numbers indicate <emph>P</emph> < 0.05.</p> <p>We next investigated whether sleep variables were associated with changes in academic major, STEM retention, or institutional withdrawal. When limiting analyses to first-year data, there were no significant associations (all <emph>P</emph> > 0.05), which likely reflected that withdrawal and attrition occurred minimally during year 1 (Table 3 and Supplemental Tables S2 and S3). However, for the year 2 data, shorter weekday TST was predictive of switching out of a STEM major/track, and this association remained significant even when controlling for first-year GPA, age, sex, BIPOC status, mental health, and fluid intelligence (Table 3). University students who persisted in the STEM pathway showed an average of 28.27 min more sleep on weeknights at baseline than students who later left STEM pathways [<emph>t</emph>(<reflink idref="bib482" id="ref45">482</reflink>) = 2.73; <emph>P</emph> = 0.007; <emph>d</emph> = 0.41]. Each additional 10 min of weekday TST was associated with a 4.9% reduction in the odds of switching away from STEM majors/tracks (Fig. 1<emph>A</emph>). This effect remained significant when additionally controlling for ACT scores [odds ratio (OR) = 0.993; 95% confidence interval (CI): 0.987, 0.998; <emph>P</emph> = 0.013]. Figure 1<emph>B</emph> further illustrates that, among the students who switched out of STEM tracks, very few reported sleeping 8–9 h during their first year.</p> <p></p> <p>Open in Viewer</p> <p>PHOTO (COLOR): Figure 1. Predictors of science, technology, engineering, and mathematics (STEM) attrition. A: probability of STEM retention at the end of year 2 in relation to baseline weekday total sleep time (TST) in first-year university students. Bars indicate mean values and error bars reflect standard errors. B: data distributions illustrated by scatter dot plots of baseline weekday TST in relation to retention/attrition of STEM pathway by end of year 2.</p> <p></p> <p>Open in Viewer</p> <p>Table 3. Logistic regression coefficients predicting switching out of STEM in the first year and second year</p> <p> <ephtml> <table><thead><tr><td /><th colspan="3">Model 1<sup>a</sup></th><th colspan="3">Model 2<sup>b</sup></th></tr><tr><td /><th rowspan="2">OR</th><th colspan="2">95% CI</th><th rowspan="2">OR</th><th colspan="2">95% CI</th></tr><tr><td /><th>Lower</th><th>Upper</th><th>Lower</th><th>Upper</th></tr></thead><tbody><tr><td><p>Year 1</p></td><td /><td /></tr><tr><td><p> PSQI global</p></td><td><p>1.131</p></td><td><p>0.982</p></td><td><p>1.302</p></td><td><p>1.130</p></td><td><p>0.960</p></td><td><p>1.329</p></td></tr><tr><td><p> Social jetlag</p></td><td><p>1.007</p></td><td><p>0.999</p></td><td><p>1.016</p></td><td><p>1.008</p></td><td><p>0.999</p></td><td><p>1.016</p></td></tr><tr><td><p> Weekday TST</p></td><td><p>0.999</p></td><td><p>0.993</p></td><td><p>1.005</p></td><td><p>1.000</p></td><td><p>0.994</p></td><td><p>1.006</p></td></tr><tr><td><p> ESS</p></td><td><p>0.980</p></td><td><p>0.879</p></td><td><p>1.093</p></td><td><p>0.972</p></td><td><p>0.869</p></td><td><p>1.086</p></td></tr><tr><td><p> MEQ</p></td><td><p>0.975</p></td><td><p>0.932</p></td><td><p>1.020</p></td><td><p>0.977</p></td><td><p>0.932</p></td><td><p>1.024</p></td></tr><tr><td><p>Year 2<sup>c</sup></p></td><td /><td /></tr><tr><td><p> PSQI global</p></td><td><p>1.051</p></td><td><p>0.935</p></td><td><p>1.182</p></td><td><p>1.066</p></td><td><p>0.935</p></td><td><p>1.216</p></td></tr><tr><td><p> Social jetlag</p></td><td><p>0.993</p></td><td><p>0.985</p></td><td><p>1.001</p></td><td><p>0.993</p></td><td><p>0.984</p></td><td><p>1.001</p></td></tr><tr><td><p> Weekday TST</p></td><td><p>0.995</p></td><td><p>0.991</p></td><td><p>0.999</p></td><td><p>0.995</p></td><td><p>0.991</p></td><td><p>0.999</p></td></tr><tr><td><p> ESS</p></td><td><p>0.971</p></td><td><p>0.894</p></td><td><p>1.055</p></td><td><p>0.969</p></td><td><p>0.890</p></td><td><p>1.054</p></td></tr><tr><td><p> MEQ</p></td><td><p>0.995</p></td><td><p>0.961</p></td><td><p>1.030</p></td><td><p>0.993</p></td><td><p>0.958</p></td><td><p>1.029</p></td></tr></tbody></table> </ephtml> </p> <p>All omnibus tests for first-year predictions were nonsignificant at α = 0.05. CI, confidence interval; ESS, Epworth Sleepiness Scale; MEQ, Morningness-Eveningness Questionnaire; OR, odds ratio; PSQI, Pittsburgh Sleep Quality Index; STEM, science, technology, engineering, and mathematics; Weekday TST, weekday total sleep time. <sups>a</sups>Adjusted for age, sex, and black indigenous people of color (BIPOC) status; <sups>b</sups>adjusted for age, sex, BIPOC status, depressive symptoms, trait anxiety, and fluid intelligence; <sups>c</sups>additionally adjusted for first-year GPA.</p> <p>STEM withdrawal is theoretically driven by poor academic performance ([<reflink idref="bib47" id="ref46">47</reflink>]), but a mediation analysis indicated that the effect of weekday TST on STEM retention was only partially mediated by GPA in year 1. For the direct effect, each 10-min increase in TST was associated with 4.4% decreased odds of leaving STEM by the end of the second year (OR = 0.9956; 95% CI: 0.9914, 0.9997; <emph>P</emph> = 0.036). For every 10-min increase, TST was also indirectly associated with a 1.2% decreased odds of leaving STEM through first-year GPA (OR = 0.9988; bootstrapped 95% CI: 0.9977, 0.9996).Most outcomes showed consistency across sex groups, BIPOC status, depressive symptoms, anxiety symptoms, and fluid intelligence (i.e., nonmoderation; Supplemental Tables S4–S6). However, depressive symptoms significantly moderated the effect of global sleep quality on first-year GPA [Supplemental Table S4; <emph>F</emph>(<reflink idref="bib3" id="ref47">3</reflink>,<reflink idref="bib461" id="ref48">461</reflink>) = 24.04; <emph>P</emph> < 0.001; <emph>R</emph><sups>2</sups> = 0.135]. Both sleep quality (<emph>b</emph> = −0.056; <emph>P</emph> < 0.001) and depressive symptoms (<emph>b</emph> = −0.0071; <emph>P</emph> = 0.019) predicted lower GPA, but the negative effect of poor sleep quality on GPA was greater among those with worse depressive symptoms (<emph>b</emph> = −0.0027; <emph>P</emph> = 0.006; see Fig. 2).</p> <p></p> <p>Open in Viewer</p> <p>PHOTO (COLOR): Figure 2. The relationship between Pittsburgh Sleep Quality Index (PSQI) global scores and first-year grade-point average (GPA), separated by high depressive [Center for Epidemiological Studies-Depression (CES-D) ≥16] and low depressive (CES-D ≤15) symptoms ([<reflink idref="bib42" id="ref49">42</reflink>]). Error bands reflect 95% confidence intervals. The vertical dotted line denotes the standard cutoff for poor global sleep quality.</p> <hd id="AN0194748791-17">DISCUSSION</hd> <p>First-year university students routinely restrict their sleep ([<reflink idref="bib9" id="ref50">9</reflink>]) below the recommended guidelines of 7–9 h/night ([<reflink idref="bib48" id="ref51">48</reflink>]), often for digital leisure and cultural/social reasons ([<reflink idref="bib49" id="ref52">49</reflink>]). Even students who are educated about the importance of sleep for cognition often engage in sleep-restricting behaviors ([<reflink idref="bib25" id="ref53">25</reflink>]) with the belief that they can effectively cram class information the night before tests ([<reflink idref="bib50" id="ref54">50</reflink>]). The current study's findings dispute this belief: first-year students with poorer sleep health demonstrated worse future academic outcomes, even when controlling for demographic, intelligence, and mental health factors.Sleep health is a multidimensional construct ([<reflink idref="bib51" id="ref55">51</reflink>]) that includes sleep duration, sleep continuity, timing, satisfaction with sleep, and daytime functioning. The existing literature is equivocal though regarding whether any of these factors predict academic performance longitudinally ([<reflink idref="bib27" id="ref56">27</reflink>]). Using a longitudinal design of first-year STEM majors, objective tracking of academic performance, and theoretically-guided covariates, we observed that shorter sleep durations, poorer sleep quality, evening preferences, and greater social jetlag were linked to worse future academic outcomes. PSQI-derived sleep quality explained the most variance in classroom performance (GPA), predicting not only first-year GPA but also second-year GPA even after controlling for students' year 1 performance (as well as demographics, intelligence, and mental health). PSQI's predictive power may be due to its incorporation of multiple measures of sleep health, including sleep duration, sleep continuity, sleep satisfaction, and daytime functioning.Social jetlag and chronotype preference were also predictive of first-year GPA. These findings converged with prior work that found higher performing students to have greater consistency in their sleep schedules ([<reflink idref="bib8" id="ref57">8</reflink>], [<reflink idref="bib53" id="ref58">53</reflink>]). Furthermore, our findings extend prior work that observed an academic advantage for morning types compared to evening types to the domain of STEM students ([<reflink idref="bib55" id="ref59">55</reflink>]). In one longitudinal study, students who switched from reporting an evening chronotype to reporting a morning chronotype across time points showed better academic performance than evening types ([<reflink idref="bib57" id="ref60">57</reflink>]). The idea here is that sleep variability and evening chronotypes can lead to circadian disruption and poor sleep health, especially when students must attend early classes ([<reflink idref="bib58" id="ref61">58</reflink>]). Other explanations are also plausible such as late-night alcohol/drug usage, poor time management, and digital displacement of studying and sleeping ([<reflink idref="bib59" id="ref62">59</reflink>]).Not all sleep measures predicted GPA. The ESS, which assesses daytime sleepiness via queries about dozing and is typically used to assist detection of sleep disorders, did not predict academic outcomes perhaps because sleep-related breathing disorders and hypersomnolence conditions are atypical among university students ([<reflink idref="bib62" id="ref63">62</reflink>]). In addition, subjective sleepiness may be masked in this student population by caffeine consumption, bright light exposure (screens), or cultural factors ([<reflink idref="bib49" id="ref64">49</reflink>], [<reflink idref="bib63" id="ref65">63</reflink>]). It was also notable that most of the sleep variables that predicted GPA in year 1 did not predict GPA in year 2 (except PSQI global scores). One possible explanation is that factors such as social jetlag may decrease in frequency or severity from the first to the second year, which would lessen the predictive value of the initial behavior on academic performance with increasing time. PSQI, on the other hand, represents global sleep quality and may be more stable over time.Most work on sleep and academic performance has focused on cumulative GPA, but by using a longitudinal design, we were also able to investigate changes in majors and withdrawal rates ([<reflink idref="bib53" id="ref66">53</reflink>], [<reflink idref="bib60" id="ref67">60</reflink>], [<reflink idref="bib64" id="ref68">64</reflink>]). We observed that baseline weekday TST was predictive of STEM retention by the end of year 2, when controlling for other variables that could influence retention (demographic, intelligence, and mental health variables). Most of the students who withdrew reported sleeping <8 h, and none reported 9 h of sleep, which may have implications for consensus guidelines on sleep duration ([<reflink idref="bib48" id="ref69">48</reflink>]). Early college students are typically recommended to sleep 7–9 h/night (young adult recommendation), but given that first-year students are at an adolescent neurodevelopmental transition point, an 8- to 10-h recommendation might be appropriate (teenager recommendation).To understand why short sleep predicted STEM retention, we first considered academic performance markers. Interestingly, weekday sleep duration continued to predict STEM retention even after accounting for first-year GPA. This observation converges with prior educational research reporting that even though GPA contributes to STEM retention, high-performing students still sometimes withdraw from STEM pathways due to competency beliefs, motivational factors, and resilience factors ([<reflink idref="bib65" id="ref70">65</reflink>]). Mild sleep restriction has been suggested to undermine resilience and motivation ([<reflink idref="bib66" id="ref71">66</reflink>]). One possibility, therefore, is that the common short-term strategy of sacrificing sleep to support high academic performance (e.g., "cramming" before a test) may have long-term consequences for health and persistence in the planned career path ([<reflink idref="bib22" id="ref72">22</reflink>]).The current study also explored potential vulnerability/resilience to poor sleep in relation to demographic, mental health, and intelligence factors ([<reflink idref="bib34" id="ref73">34</reflink>]). In general, the sleep-academic relationship was consistent across sex, race/ethnicity, fluid intelligence, and anxiety. However, one moderation test showed a strong effect size: the predictive strength of global sleep quality for GPA was greater in students with higher levels of depressive symptoms. This finding converges with a study of college students, which also observed that well-being moderated the relationship between sleep quality and GPA ([<reflink idref="bib34" id="ref74">34</reflink>]). Replication work is needed to determine the reproducibility of this moderation outcome, but conceptually, the finding aligns with the view that sleep loss exacerbates mental health symptoms, which subsequently undermines functioning ([<reflink idref="bib4" id="ref75">4</reflink>], [<reflink idref="bib68" id="ref76">68</reflink>]).Study limitations included, first, an observational design, which prevents causal inferences. The impact of this limitation is partially offset by the longitudinal approach in a robust number of first-year STEM-focused college students (<emph>n</emph> = 489; Ref. [<reflink idref="bib27" id="ref77">27</reflink>]). Second, the sample was composed of STEM students, which may not generalize to how sleep relates to retention and performance of students in other majors/tracks. Third, sleep was measured with self-report scales, which do not always align with actigraphy or polysomnography ([<reflink idref="bib61" id="ref78">61</reflink>], [<reflink idref="bib69" id="ref79">69</reflink>]). It is important to note though that if sleep measures are to be widely adopted by universities (e.g., identifying at-risk students), such institutions are most likely to adopt self-report measures due to their ease, efficiency, and low-cost.In conclusion, our findings converge with laboratory-based research indicating that short or poor quality sleep can be harmful to cognitive functioning. First-year students are unlikely to flourish at their universities without consistent sleep of sufficient duration and quality. University administrators should consider implementing programs such as sleep education with goal setting ([<reflink idref="bib20" id="ref80">20</reflink>]), Cognitive Behavioral Therapy for Insomnia (CBT-I)-based resources ([<reflink idref="bib70" id="ref81">70</reflink>]), resources for environmental improvement (e.g., residence hall modifications), and revisiting policies on early classroom instruction and late assignment deadlines that can undermine sleep health in students.</p> <hd id="AN0194748791-18">ETHICAL APPROVALS</hd> <p>The authors confirm that the research presented in this article met the ethical guidelines, including adherence to the legal requirements, of the United States and received approval from the Institutional Review Board of Baylor University.</p> <hd id="AN0194748791-19">DATA AVAILABILITY</hd> <p>The data are available at Open Science Framework (https://osf.io/jmavn/).</p> <hd id="AN0194748791-20">SUPPLEMENTAL MATERIAL</hd> <p>Supplemental Tables S1–S6: https://osf.io/jmavn/.</p> <hd id="AN0194748791-21">ACKNOWLEDGMENTS</hd> <p>The authors are grateful to Dr. Alex Beaujean and Dr. Andrew Gallucci for feedback.</p> <hd id="AN0194748791-22">GRANTS</hd> <p>This work was supported by National Science Foundation Grants 1920730 and 1943323.</p> <hd id="AN0194748791-23">DISCLOSURES</hd> <p>No conflicts of interest, financial or otherwise, are declared by the authors.</p> <hd id="AN0194748791-24">AUTHOR CONTRIBUTIONS</hd> <p>C.L.F. and M.K.S. conceived and designed research; C.L.F. performed experiments; C.L.F. and M.K.S. analyzed data; C.L.F., J.R.C., and M.K.S. interpreted results of experiments; C.L.F. and M.K.S. prepared figures; C.L.F. and M.K.S. drafted manuscript; C.L.F., J.R.C., and M.K.S. edited and revised manuscript; C.L.F., J.R.C., and M.K.S. approved final version of manuscript.</p> <ref id="AN0194748791-25"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Lund HG, Reider BD, Whiting AB, Prichard JR. 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  Data: Sleep and Circadian Predictors of Academic Performance and Retention in STEM Pathways: A Longitudinal Study in University Freshmen
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  Data: <searchLink fieldCode="SO" term="%22Advances+in+Physiology+Education%22"><i>Advances in Physiology Education</i></searchLink>. 2026 50(2):394-401.
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  Data: American Physiological Society. 9650 Rockville Pike, Bethesda, MD 20814-3991. Tel: 301-634-7164; Fax: 301-634-7241; e-mail: webmaster@the-aps.org; Web site: https://www.physiology.org/journal/advances
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  Label: Abstract
  Group: Ab
  Data: Poor sleep health is common among university students, but there are diverging viewpoints on whether their sleep loss helps, harms, or has no impact on academic performance. We investigated whether sleep health markers in first-year university students predicted longitudinal academic outcomes when accounting for key variables. First-year university students who were pursuing a science, technology, engineering, and mathematics (STEM) career pathway (n = 489) were recruited to complete a baseline session that included measures of global sleep quality, chronotype, daytime sleepiness, social jetlag (change in sleep timing from weekdays to weekends), demographics, mental health, and fluid intelligence (reasoning). At the end of year 1 and year 2, we extracted data on cumulative grade-point average (GPA), academic major change, STEM pathway change, and institutional withdrawal. After adjusting for demographic, mental health, and fluid intelligence factors, we observed that worse global sleep quality, evening chronotype, and worse social jetlag independently predicted year 1 GPA. Global sleep quality also predicted year 2 GPA, even when accounting for prior academic performance. Students with shorter sleep durations were more likely to switch from their STEM career pathway, even when accounting for academic performance, demographics, mental health, and fluid intelligence. In conclusion, sleep health markers are predictive of better future academic performance and retention in STEM pathways. There is a need for individual and environmental interventions to improve sleep health in first-year students and to determine causal direction.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: Note
  Label: Notes
  Group: Note
  Data: https://osf.io/jmavn
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1502785
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1502785
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1152/advan.00313.2025
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 394
    Subjects:
      – SubjectFull: Sleep
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Academic Achievement
        Type: general
      – SubjectFull: STEM Education
        Type: general
      – SubjectFull: College Freshmen
        Type: general
      – SubjectFull: Grade Point Average
        Type: general
      – SubjectFull: Academic Persistence
        Type: general
      – SubjectFull: Majors (Students)
        Type: general
      – SubjectFull: Texas
        Type: general
      – SubjectFull: Center for Epidemiologic Studies Depression Scale
        Type: general
      – SubjectFull: State Trait Anxiety Inventory
        Type: general
      – SubjectFull: Raven Advanced Progressive Matrices
        Type: general
    Titles:
      – TitleFull: Sleep and Circadian Predictors of Academic Performance and Retention in STEM Pathways: A Longitudinal Study in University Freshmen
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Corinne L. Fitzsimmons
      – PersonEntity:
          Name:
            NameFull: Jason R. Carter
      – PersonEntity:
          Name:
            NameFull: Michael K. Scullin
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 1043-4046
            – Type: issn-electronic
              Value: 1522-1229
          Numbering:
            – Type: volume
              Value: 50
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Advances in Physiology Education
              Type: main
ResultId 1