Abstract Introduction College students face unique factors (e.g., academic and social demands, varied class schedules across days) that may put them at greater risk for mental health and sleep difficulties. Prior studies have shown that depression, anxiety, and stress are associated with sleep disturbances, but more studies utilizing repeated measures are needed to understand these relationships across more granular timescales. This study examined daily associations among stress, depression, anxiety, and sleep in college students. Methods 333 college students (Mage = 20 years, 54% Female, 81% Non-Hispanic/Latinx) from two universities in Tucson, AZ, and Corvallis, OR, completed 14-days of the Consensus Sleep Diary (CSD), Perceived Stress Scale-4 (PSS-4), Generalized Anxiety Disorder 2-item (GAD-2), and Patient Health Questionnaire-2 (PHQ-2). Multilevel models examined how stress (PSS-4 total), anxiety symptoms (GAD-2 total), and depression symptoms (PHQ-2 total) were associated with same-day sleep efficiency, duration, and timing. Results Between-person analyses revealed that, on average, students with greater depression reported lower sleep efficiency (β = −0.87, SE = 0.41, p = 0.037), while those with greater stress reported shorter sleep duration (β = −4.06, SE = 1.88, p = 0.031). Within-person analyses revealed that, when students experienced greater than usual anxiety symptoms, they reported worse sleep efficiency (β = −0.41, SE = 0.16, p = 0.010) and lower sleep duration (β = −4.20, SE = 1.85, p = 0.023) on the same day. When students experienced greater than usual stress, they reported shorter sleep duration (β = −2.07, SE = 0.94, p = 0.027). When students experienced greater than usual depressive symptoms, they reported longer sleep duration (β = 5.39, SE = 2.05, p = 0.009) and later sleep midpoint (β = 15.00, SE = 5.69, p = 0.009). Conclusion Results reveal that daily elevations in stress and anxiety are associated with shorter or less efficient sleep, whereas elevation in depressive symptoms is associated with longer and later sleep in college students. Findings extend past research by highlighting the utility of using repeated measurements to characterize the differential relationships among stress, anxiety, depression, and sleep in college students. Support (if any)

Structured clinical interviews are the optimal means for assessing certain sleep diagnoses, yet no such interview existed. To address this gap, the Structured Clinical Interview for Sleep Disorders (SCISD) was developed to evaluate DSM-5 sleep disorders. The SCISD-Revised (SCISD-R) was updated to align with DSM-5-TR criteria but has not yet undergone psychometric evaluation. This study, funded by the AASM Junior Investigator’s Award, focuses on assessing the inter-rater reliability of the SCISD-R to confirm its utility in both clinical and research contexts. Data for this study were obtained from a randomized clinical trial evaluating different modalities of Cognitive Behavioral Therapy for Insomnia in adults aged 50–65 years. The sample is primarily White (88%), female (83%), and non-Hispanic (88%), with an average age of 57.99 years (SD = 4.92). A random sample of 100 SCISD-R interviews, conducted by trained clinicians, was selected for double-rating. Each interview consists of a general information section and modular assessments aligned with DSM-5-TR criteria. The double-rating process is ongoing, and inter-rater reliability will be assessed using the percent agreement and Cohen’s Kappa. The SCISD-R is predicted to have high inter-rater reliability across modules with sufficient prevalence, similar to the SCISD for DSM-5. Preliminary data (n = 47) show strong consistency in clinician ratings for insomnia (97%), circadian disorders (82%), and obstructive sleep apnea (77%). Data analysis will be completed by January 2025. The SCISD-R is expected to demonstrate robust inter-rater reliability, supporting its future use as a reliable diagnostic tool for DSM-5-TR sleep disorders. These findings will provide a critical foundation for upcoming research and enhance assessment in clinical settings, paving the way for broader validation studies and adaptations for diverse populations. This study was supported by the American Academy of Sleep Medicine Focused Projects for Junior Investigators Award (PI: Campbell).

Sleep difficulties are common among college student athletes and can significantly affect their physical and mental well-being. The Athlete Sleep Screening Questionnaire (ASSQ) is a screener for detecting sleep disturbances. Research has shown that poor sleep quality mediates the relationship between stress and maladaptive health behaviors like poor diet and alcohol misuse. The present study explores the mediating effect of sleep difficulties on perceived stress and drug use in college student athletes. College students (N=1886; Mage=20.54 years, 57% female, 73.80% Caucasian) from two universities completed self-report questionnaires on the following domains: sleep disturbance (ASSQ-SDS), perceived stress (PSS), and drug use (CAGE-AID). Mediation analysis was used to investigate the effect of sleep disturbances on perceived stress and drug use. The overall regression model was significant, F(3, 1352) = 21.9656, p <.001, R-squared=.0465. Sleep disturbance severity significantly predicted drug use, B = -.0834, SE =.0420, t = -1.9869, p =.047. Perceived stress did not significantly predict drug use, B = -.0102, SE =.0153, t = -.6654, p =.5059. The interaction between sleep disturbance severity and perceived stress significantly predicted drug use, B =.0056, SE =.0019, t = 2.9244, p =.0035, Delta R-Squared=.0060, F(1, 1352) = 8.5519. The results of this analysis highlight the mediating role that sleep disturbance has on reported perceived stress and drug use. Additionally, higher levels of sleep disturbance are associated with increased drug use. Athletes experiencing more sleep difficulties may have a heightened vulnerability to drug use when exposed to stress. Previous research suggests that strategies like sleep hygiene education, relaxation techniques, mindfulness, and CBTi are effective in improving sleep. Improving sleep quality may mitigate the negative effects of stress on drug use behavior, a critical focus for student health initiatives. Pacific-12 Conference Student-Athlete Health and Well-being Initiative Grant

Mild Traumatic Brain Injuries (mTBIs) are associated with symptoms such as headaches, fatigue, mood changes, cognitive dysfunction, hindering executive functioning, attention, processing speed, and memory. Prior research has also implicated mTBIs in the development of sleep dysfunction and circadian rhythm disorders, and this has been associated with further neurocognitive degeneration. However, a lack of research exists examining how sleep dysfunction in individuals with mTBIs impacts specific cognitive domains. This study aims to examine the relationship and influence that sleep has on mTBI symptomology and cognitive dysfunction. Adults with mTBIs (N= 167; Mage=24.41 years, 59% female) completed self-report questionnaires that assessed symptomology associated with mTBIs (RPCSQ), sleep (DSIQ), and personality (PAI; used to assess thought dysfunction). Participants also completed cognitive testing to assess executive functioning (D-KEFS). A multivariate analysis of variance assessed concussive symptomology’s association with cognitive domains and sleep disturbance. Moderation analyses assessed sleep’s influence on the relation between symptomology and cognitive dysfunction. There was a significant effect on cognitive dysfunction and sleep based on mTBI symptomology: PAI Thought dysfunction: F(1, 42) = 2.015, p =.002, partial η2 =.463 D-KEFS Number-Letter Switching: F(1, 42) = 2.129, p =.001, partial η2 =.477 D-KEFS Color Word Interference: F(1, 42) = 2.115, p =.001, partial η2 =.476 Sleep disturbance: F(1, 42) = 1.929, p =.004, partial η2 =.453 Sleep disturbance did not moderate the relationship between mTBI symptomology and cognitive task performance. Results of this study replicate previous research that suggests mTBIs impact sleep and cognitive domains, like concentration, confusion, disorganization, and executive functioning. Despite previous literature on sleep and cognition, it indicates sleep does not moderate the relationship between mTBIs and cognitive dysfunction, suggesting an alternative relational mechanism. Future research should examine sleep’s role in cognitive dysfunction in mTBIs given its potential neurodegenerative effects. This project was funded by a USAMRAA Grant (W81XWH-12-1-0386) to WDSK

The recommended first-line treatment for insomnia is cognitive behavioral therapy for insomnia (CBTi), but access is limited. Telehealth- or internet-delivered CBTi are alternative ways to increase access. To date, these intervention modalities have never been compared within a single study. Further, few studies have examined (a) predictors of response to the different modalities, (b) whether successfully treating insomnia can result in improvement of health-related biomarkers, and (c) mechanisms of change in CBTi. This protocol was designed to compare the three CBTi modalities to each other and a waitlist control for adults aged 50–65 years ( N = 100). Participants are randomly assigned to one of four study arms: in-person- ( n = 30), telehealth- ( n = 30) internet-delivered ( n = 30) CBTi, or 12-week waitlist control ( n = 10). Outcomes include self-reported insomnia symptom severity, polysomnography, circadian rhythms of activity and core body temperature, blood- and sweat-based biomarkers, cognitive functioning and magnetic resonance imaging.

Sleep timing (e.g. sleep midpoint) plays a significant role in mental health. Whereas person-level mean sleep midpoint has been linked to depression, anxiety, and stress, the impact of daily variability in sleep midpoint is underexplored. This study investigated whether within- and between-person variability in sleep timing predicted mental health outcomes among young adult college students. Participants (N = 261, age M = 19.92 [4.79]) completed daily diaries over two weeks, providing data on sleep parameters and mental health symptoms (i.e., depression, anxiety, stress). Sleep midpoint was calculated as total sleep time [TST] + (sleep onset latency [SOL] + wake after sleep onset [WASO])/2 which was converted to hours centered on midnight. SAS PROC MIXED was used to run multilevel analyses with maximum likelihood estimation and Kenward-Roger degrees of freedom to model the effect of previous day sleep midpoint (level 1) and mean sleep midpoint (level 2) on mental health symptoms. Later mean sleep midpoint had worse depressive symptoms (β = 0.18, p<.001), anxiety symptoms (β = 0.20, p <.001), and stress (β = 0.36, p = 0.005). Within person analyses revealed later daily sleep midpoint predicted less anxiety the next day (β = -0.04, p = 0.02) but was not associated with depression or stress. Gender explained some effects, with women showing stronger associations between mean sleep midpoint, anxiety and stress. Young adult college students with later average sleep timing had poorer mental health symptoms. Daily anxiety was reduced on days following later sleep timing relative to a person’s baseline, potentially reflecting adaptive behaviors like socializing or study-related activities. This suggests complex relationships between sleep timing and mental health. Further research is needed to determine whether these associations are influenced by chronotype or external factors, such as academic demands or conscientiousness. Moreover, studies promoting earlier and more consistent sleep timing should assess their potential benefits for mental health, particularly among young adults. This study was supported by a Pac-12 Student-Athlete Health and Well-Being Initiative Grant and an in-kind grant from Fitbit Inc. Dr. Dietch’s effort on this project was supported by a National Heart, Lung and Blood Institute grant 1K23HL157698.

Insomnia is defined as difficulty falling or staying asleep or waking earlier than desired with inability to return to sleep. Insomnia Identity is described as one’s identification as “an insomniac,” which can be measured independently of other sleep parameters or a diagnosis of insomnia disorder. This study investigated whether pathologizing sleep concerns through endorsement of Insomnia Identity may alter treatment outcomes. Participants (N=87, 71 females, Mage=57.69±4.88 years) completed questionnaires about Insomnia Identity (InsID) and insomnia severity (ISI) pre- and post-cognitive behavioral therapy for insomnia (CBTi) in a 4-arm, randomized clinical trial with a 3:3:3:1 allocation ratio: in-person, telehealth, SHUTi, and waitlist. Change in InsID ratings after treatment and the relationships between baseline InsID and post-treatment ISI were examined. ANOVA results indicated participants did not differ between group on pre-treatment (BL) ISI or InsID ratings (p’s>.05). There were significant differences between groups for ISI at post-treatment (PT), F (3,72)=4.09, p=.010, specifically between waitlist (mISI=14.57), in-person (mISI=7.22), and telehealth (mISI=7.17), but not SHUTi (mISI=8.52). There were significant differences between groups for PT InsID, F (3,72) = 3.52, p=.019, specifically between waitlist (mInsID=5.14), in-person (mInsID=2.91), telehealth (mInsID=2.96), and SHUTi (mInsID=2.90). The three treatment groups did not significantly differ for either analysis. Linear regression was used to determine if InsID ratings pre-treatment predicted post-treatment ISI and ISI pre-post change scores within the treatment groups only. Elevated pre-treatment InsID predicted higher posttreatment-ISI scores, F (1,65)=8.17, p=.006, R^2=.11, β=.34, but did not differ between groups (p=.14). InsID did not predict ISI change scores (p=.82). Results indicate that Insomnia Identity is modifiable with CBTi treatment and appears to be related to the overall outcome of insomnia severity. Future treatment focused on reducing Insomnia Identity may lead to improved treatment outcomes. University of Arizona

Cognitive behavioral therapy for insomnia (CBTi) is considered the first-line treatment for insomnia, but access to this treatment is limited. Telehealth- or Internet-delivered CBTi are potential strategies for increasing access. This study investigated the perceived credibility and expectancy of treatment success across three CBTi modalities and explored the effects of credibility and expectancy has on treatment outcomes. Sixty-two participants (84% Females, Mage=57.90 years; SD=4.75) between the ages of 50-65 who reported complaints of insomnia were recruited for this study. Participants completed the Credibility Expectancy Scale (CEQ) at the first session and the Insomnia Severity Index (ISI) pre- and post-treatment. Participants were randomly assigned into one of the three CBTi modalities: In-Person (n=21), Telehealth (n=22), and Internet (SHUTi; n=19) delivered CBTi. CEQ variables were separated into a credibility score, expectations based on feelings score, and expectations based on thoughts score. Differences between CEQ scores of credibility and expectations were compared across CBTi modalities, using one-way analyses of variance. Credibility scores were significantly higher for In-Person (M=7.59, SD=1.10) and Telehealth (M=7.36, SD=1.23) when compared to Internet-delivered CBTi (M=5.86, SD=1.74, p’s <.05). Expectancy (thought-based) scores were significantly higher for Telehealth CBTi (M=6.41, SD=1.59) when compared to Internet-delivered CBTi (M=4.89, SD=2.31, p’s <.05). All other comparisons did not meet statistical significance. A multiple linear regression analysis resulted in a significant model, R2=.23, F(1, 60)=18.18, p <.001. Further analysis revealed that the measure of expectancy based on feeling was the only significant predictor of posttreatment ISI scores, β=-.48, p <.001. Results reveal that patients expect more improvements from CBTi modes that involve a clinician. The pre-treatment expectancy predicts post-treatment ISI only with expectancy based on feelings, which indicates that expectations of CBTi outcomes based on thoughts and feelings are different, this may be affecting the overall treatment outcomes. Future research could explore this further. Results suggest that Telehealth CBTi may be an effective strategy to increase the accessibility of treatment for insomnia. University of Arizona

The validity of self-report measures may be impacted when individuals feel obligated to respond in a way that represents themselves favorably. This is known as social-desirability bias. Prior research has examined social desirability effects on academic achievement reports (e.g., GPA) and financial behavior. However, little research has explored the role of social desirability in self-reported mental health. This study aims to investigate the effect of social desirability on multiple mental health screeners. College students (N=1886; Mage=20.54 years, 57% female, 73.80% Caucasian) from two universities completed self-report questionnaires on the following domains: social desirability (SDRS-5), sleep disturbance (ASSQ-SDS), generalized anxiety disorder (GAD-7), major depressive disorder (PHQ-9), and posttraumatic stress disorder (PC-PTSD-5). Multivariate multiple regression was used to examine social desirability on the total scores of the mental health screeners. The overall multivariate multiple regression model was significant, F(4,1331) = 5.507, p <.001, Wilks’ lamba = 0.984. Social desirability predicted symptoms of PTSD (PCL-5, F(1, 1334) = 14.985, p <.001, B = -1.378), generalized anxiety (GAD-7, F(1, 1334) = 16.375, p <.001, B = -.462), and major depression (PHQ-9, F(1, 1334) = 19.461, p <.001, B = -.550), but not sleep disturbance (ASSQ-SDS, F(1, 1334) = 2.549, p =.111, B = -.109). The results of this study highlight the role that social desirability plays in self-reported sleep disturbance and mental health symptomology. While social desirability significantly predicted lower scores on self-reported mental health symptoms, its effect on sleep disturbances was not statistically significant. This suggests that sleep-related concerns may be perceived differently or are potentially less stigmatized than other mental health concerns. Future research may explore whether the relationship between social desirability and sleep reporting varies based on symptom severity, chronicity, or comorbid mental health concerns. Pacific-12 Conference Student-Athlete Health and Well-being Initiative Grant

Sleep disturbances and mental health concerns are highly prevalent among college students, but it is unknown whether college athletics may be a protective or risk factor for sleep or mental health concerns. Therefore, we aimed to investigate whether insomnia severity, diary- and wearable-derived sleep, and mental health symptoms differ in college student athletes versus non-athletes. Participants were 144 college student athletes and 195 college student non-athletes recruited from two universities in the Pac-12 athletic conference (total N=339). Participants completed a baseline survey, 14-days of daily diaries, and wore a wearable device (Fitbit Sense) for 28 days. Statistical analysis was conducted using t-tests and linear regression models adjusted for age and gender. ISI scores were significantly lower/better (d=.34, p=.002) in college student athletes (M=6.4[4.6]) compared to non-athletes (M=8.0[4.9]). Diary- and wearable-derived sleep midpoint was significantly earlier in college student athletes (diary: M=3:31[0:55], wearable: M=3:48[1:01]) compared to non-athletes (diary: M=4:37[1:18], d=.92, p<.001, wearable: M=4:45[1:16], d=.80, p<.001). Diary- and wearable-derived SOL, WASO, TWAK, TIB, TST, SE, sleep quality, and sleep medication use did not significantly differ between athletes and non-athletes. All mental health variables were significantly better in college student athletes (PSS-4: M=4.1[2.3]; GAD-2: M=.7[.8]; PHQ-2: M=.4[.7]; PROMIS-Fatigue SF: M=7.8[2.1]) compared to non-athletes (PSS-4: M=5.3[2.6], d=.49, p<.001; GAD-2: M=1.5[1.2], d=.70, p<.001; PHQ-2: M=1.0[1.2], d=.62, p<.001; PROMIS-Fatigue SF: M=8.9[3.0], d=.41, p<.001). Caffeine and alcohol use were significantly lower in college student athletes (caffeine: M=.7[.9]; alcohol: M=.1[.3]) versus non-athletes (caffeine: M=.9[.9], d=.27, p=.028; alcohol: M=.3[.6], d=.34, p=.001). Athlete status remained a significant predictor of all outcomes described above (p’s<.05), after adjusting for age and gender. Participation in collegiate athletics may be protective against developing insomnia and mental health symptoms, potentially attributable to earlier sleep schedules, increased physical activity, and/or additional support, social resources, and monitoring provided to college athletes. Future directions include investigating daily relationships between sleep timing and mental health symptoms in athletes and non-athletes. This study was supported by a Pac-12 Student-Athlete Health and Well-Being Initiative Grant and an in-kind grant from Fitbit Inc. Dr. Dietch’s effort on this project was supported by a National Heart, Lung and Blood Institute grant 1K23HL157698.

Psychometric properties of the Insomnia Severity Index (ISI) were analyzed in U.S. college samples. ISI items and total score with sleep and psychosocial questionnaires were examined in Experiment I. ISI diagnostic accuracy in a clinical sample with and without insomnia was assessed in Experiment II. ISI test-retest validity, confirmatory factor analysis (CFA), and item response theory via graded response model (GRM) were assessed in Experiment III. Results indicated analogous ISI and sleep diary items showed moderate correlations (r(1) = .40; r(2) = .45). The ISI total had weak to strong correlations with other indicators of sleep-related disturbance (rs = .25-.62). The ISI had weak to moderate correlations with psychosocial measures commonly associated with insomnia (rs = .10-.57). The diagnostic accuracy of the ISI was very high (area under the curve [AUC] = .999). Sensitivity and specificity were maximized at a cutoff score >= 8. The ISI demonstrated good test-retest reliability (ICC = .87). CFA revealed a three-factor model for two study samples and GRM indicated better ability of the ISI to assess moderate (Sample III) and moderate to high (Sample I) levels of insomnia severity. The ISI demonstrated good psychometric properties and appears generally valid for screening insomnia disorder and assessing insomnia severity in college students. Overlap with psychological symptoms suggests caution while interpreting these constructs independently.

Abstract Introduction Differences between objectively and subjectively measured sleep can vary widely between participants and these differences may depend on individual characteristics, including mental health. The severity of mental health symptoms may be beneficial in assessing the magnitude of discrepancies between participants’ objective and subjective data. This secondary data analysis examined the predictivity of validated measures of post-traumatic stress symptoms, anxiety symptoms, and depressive symptoms on differences between sleep electroencephalogram (EEG) and actigraphy with sleep diary data. Methods Adults in the community (N=80; Mage=32.65 years, 63% female, 88.8% White) completed the Post-Traumatic Stress Disorder Checklist, State-Trait Anxiety Inventory, Quick Inventory of Depressive Symptomatology, and seven days of sleep assessment via sleep diaries, actigraphy, and EEG. The mean absolute value of differences between the sleep diary, actigraphy, and EEG data was calculated for total sleep time, time in bed (TIB), sleep efficiency, sleep onset latency (SOL), wakefulness after sleep onset, terminal wakefulness, and number of awakenings. Stepwise linear regression was used to examine whether the anxiety, post-traumatic stress, and depressive symptom scores were significant predictors of the objective-subjective differences between these sleep parameters. Results Depressive symptoms significantly predicted differences between EEG and sleep diary data for SOL, F(1,72)=9.958, p=.002, R2=0.121. Anxiety symptoms significantly predicted differences between actigraphy and sleep diary data for SOL, F(1,69)=6.335, p=.014, R2=0.084. Anxiety and post-traumatic stress symptoms significantly predicted differences between actigraphy and sleep diary data for TIB, F(2,70)=10.355, p<.001, R2=.228, βQIDS=0.62, βPCL=-.42. Conclusion Anxiety, post-traumatic stress, and depressive symptoms significantly predicted EEG and actigraphy objective-subjective differences for SOL and TIB. To better assess whether these variations are due to measurement type or if there are individual characteristics responsible for the discrepancies (i.e., sleep state misperception, symptom of mental health), a larger sample with more longitudinal data is needed. Additionally, future studies may focus on clinical samples. For instance, SOL and TIB variations may be indicative of mental health concerns (i.e., hyperarousal, anxiety, depression). Support (if any) National Institutes of Health/National Institute of Allergy and Infectious Disease (R01AI128359-01), the Foundation for Rehabilitation Psychology, and the General Sleep Corporation.

Abstract Introduction Of 34M US adults affected by insomnia, 75% are older adults. Cognitive Behavioral Therapy for Insomnia (CBTi) is recommended because polypharmacy and fall risks accompany pharmacotherapies. We evaluated telehealth CBTi with an interactive patient-therapist application, SleepSpace, which integrates data from wearables and Internet of Things (IoT) devices. Methods This RCT (NCT05015803) followed community-dwelling participants 60-90 years old with an Insomnia Severity Index (ISI) score ≥11. Absence of mild cognitive impairment was affirmed with the Montreal Cognitive Assessment (MoCA) Blind v.8 (score ≥ 18). Participants wore actigraphy and an Apple Watch throughout and independently completed a weekly electronic ISI. They attended 7 weekly, ~1hr video-conference sessions (1 intake, 6 procedural) with a clinical therapist. Participants were randomly assigned to one of 3 study conditions (age-, gender-stratified): 1) education about sleep hygiene only (20%; “Hygiene”), 2) telehealth CBTi (40%; “CBTi”), and 3) telehealth CBTi with phone/IoT platform application enhancement (40%; “CBTi+”) including meditations, sound machines, smart light bulbs, an electronic diary, with visualizations, metrics, and wearable data shared with participants in the CBTi+ condition. Linear mixed models compared ISI change across time by group. Results Of 60 individuals enrolled, 54 were randomized and retained (39F, mean±SD age=71±4y). ISI slopes for both CBTi (-.09/day) and CBTi+ (-.09/day) declined at a significantly steeper rate than Hygiene (-.05/day; each p<.05), but did not differ from one another. Significantly more CBTi+ participants exhibited full remission (ISI < 8; 18/21, 85.7%) than in the Hygiene group (5/11, 45.4%; p=.03 Fisher’s Exact); CBTi alone (16/22, 72.7%) did not significantly differ from Hygiene, although with limited statistical power. Diary-reported sleep measures to calculate self-reported sleep efficiency (sSE) in the final week at end of treatment revealed differences in mean±SD for Hygiene (81±05%) vs. CBTI (88±07%), and vs. CBTI+ (90±04%, p< 0.05, t-test). Conclusion This research supports the efficacy of a remote, technology-assisted telehealth CBTi platform to improve insomnia symptoms comparable to standard telehealth-CBTi in older adults with insomnia. The platform provides enhanced data access for therapists and opportunities for data-driven engagement with patients. Support (if any) R44 AG056250, UL1TR002014

Abstract Introduction College student athletes experience unique factors in addition to those of being a traditional college student that may exacerbate sleep difficulties and/or mental health difficulties which may in turn negatively affect sleep (e.g., competition pressures, training, travel demands). Previous research has found that mental health problems are common in this population, however, less attention has been given to the evaluation of sleep disorders. This study provides an evaluation of sleep disorder questionnaires and comparison with a structured clinical sleep disorders interview (SCISD-R). Methods College student athletes (N = 114) from two Universities completed self-report questionnaires and participated in a clinical interview. Average total scores were calculated for the Epworth Sleepiness Scale (ESS), Nightmare Disorders Index (NDI), Reduced Composite Scale of Morningness (rCSM), Sleep Condition Indicator (CSI), Shiftwork Disorder Index (SWDI), STOP-Bang Questionnaire, PROMIS Sleep Disturbance B (SD) and Sleep Related Impairment (SRI), and International RLS Rating Scale (IRLS-RS). Diagnostic criteria were calculated for the SCISD-R, NDI, SCI, and SWDI. Endorsement of Insomnia Identity (“I am an Insomniac”) was obtained. Results Averages and standard deviations are as follows: ESS = 6.75±3.16; NDI = 2.83±3.38; rCSM = 20.42±2.92; SCI = 24.87±5.92; SWDI = 3.11±2.03; STOP-Bang = 1.28±1.06; PROMIS SD = 18.05±6.35; PROMIS SRI = 17.37±6.46; IRLS-RS = 2.70±4.62. Diagnostic criteria for assessments and clinical interview with shared symptoms are as follows: Nightmare disorder: 4 participants met criteria on the NDI and 3 met criteria on the SCISD-R. Insomnia disorder: 1 participant met criteria on the SCI and 10 met criteria on the SCISD-R. Circadian disorder - Shiftwork type: 11 participants met criteria for on the SWDI and 1 met criteria on the SCISD-R. Additionally on the SCISD-R, 5 participants met for hypersomnolence, 5 met for possible obstructive sleep apnea, and 1 met for possible narcolepsy. 6 participants endorsed an insomnia identity. Conclusion College student athletes endorse symptoms across a variety of sleep disorders. Differences in the number of participants who met criteria based on assessment measures versus the structured clinical interview indicate that alternate cutoff scores may be warranted for this population. Support (if any) Pacific-12 Conference Student-Athlete Health and Well-Being Initiative Grant

Aging populations are at increased risk of sleep deficiencies (e.g., insomnia) that are associated with a variety of chronic health risks, including Alzheimer's disease and related dementias (ADRD). Insomnia medications carry additional risk, including increased drowsiness and falls, as well as polypharmacy risks. The recommended first line treatment for insomnia is cognitive behavioral therapy for insomnia (CBTi), but access is limited. Telehealth is one way to increase access, particularly for older adults, but to date telehealth has been typically limited to simple videoconferencing portals. While these portals have been shown to be non-inferior to in-person treatment, it is plausible that telehealth could be significantly improved. This work describes a protocol designed to evaluate whether a clinician-patient dashboard inclusive of several user-friendly features (e.g., patterns of sleep data from ambulatory devices, guided relaxation resources, and reminders to complete in-home CBTi practice) could improve CBTi outcomes for middle-to older-aged adults (N = 100). Participants were randomly assigned to one of three telehealth interventions delivered through 6-weekly sessions: (1) CBTi augmented with a clinician patient dashboard, smartphone application, and integrated smart devices; (2) standard CBTi (i.e., active comparator); or (3) sleep hygiene education (i.e., active control). All participants were assessed at screening, pre study evaluation, baseline, throughout treatment, and at 1-week post-treatment. The primary outcome is the Insomnia Severity Index. Secondary and exploratory outcomes span sleep diary, actiwatch and Apple watch assessed sleep parameters (e.g., efficiency, duration, timing, variability), psychosocial correlates (e.g., fatigue, depression, stress), cognitive performance, treatment adherence, and neurodegenerative and systemic inflammatory biomarkers.

ObjectivesTo examine the internal consistency reliability and measurement invariance of a questionnaire battery designed to identify college student athletes at risk for mental health symptoms and disorders. MethodsCollege student athletes (N=993) completed questionnaires assessing 13 mental health domains: strain, anxiety, depression, suicide and self-harm ideation, sleep, alcohol use, drug use, eating disorders, attention deficit hyperactivity disorder (ADHD), bipolar disorder, post-traumatic stress disorder (PTSD), gambling and psychosis. Internal consistency reliability of each measure was assessed and compared between sexes as well as to previous results in elite athletes. Discriminative ability analyses were used to examine how well the cut-off score on the strain measure (Athlete Psychological Strain Questionnaire) predicted cut-offs on other screening questionnaires. ResultsStrain, anxiety, depression, suicide and self-harm ideation, ADHD, PTSD and bipolar questionnaires all had acceptable or better internal consistency reliability. Sleep, gambling and psychosis questionnaires had questionable internal consistency reliability, although approaching acceptable for certain sex by measure values. The athlete disordered eating measure (Brief Eating Disorder in Athletes Questionnaire) had poor internal consistency reliability in males and questionable internal consistency reliability in females. ConclusionsThe recommended mental health questionnaires were generally reliable for use with college student athletes. To truly determine the validity of the cut-off scores on these self-report questionnaires, future studies need to compare the questionnaires to a structured clinical interview to determine the discriminative abilities.

Abstract Introduction Short sleepers (< 6 hours per night) represent a unique phenotype of insomnia associated with more severe biological implications compared to insomnia with normal sleep duration. A primary contributor to shorter sleep duration is more time spent awake after sleep onset (i.e., WASO), which has deleterious effects on long-term cardiac physiology. For instance, short sleepers with higher WASO have lower parasympathetic activity at sleep onset compared to those with lower WASO or greater sleep duration. Methods Results presented here are derived from baseline assessments collected as a part of a clinical trial comparing three modalities of cognitive behavioral therapy for insomnia in middle-aged adults (50-65yrs) with chronic insomnia (N = 23). During their baseline assessment, individuals first completed one night of in-home polysomnography. Total sleep time was PSG derived. The following night, they wore an Equivital Harness equipped with an electrocardiogram (ECG) for 24 hours. In this investigation, inter-beat intervals were derived from ECG in 10-minute segments just before and just after bedtime (i.e., when the individual tried to sleep). WASO was calculated using sleep diaries during the night they wore the Harness and the following week. Results Preliminary data N = 23) show that high frequency (0.15-0.4Hz) heart rate variability (HRV; i.e., parasympathetic activity) at bedtime is not associated with WASO that night or WASO averaged across the following week. However, when the sample was narrowed to short sleepers only (□6hours; n = 7), lower levels of bedtime HRV (B = -.81, p = .028) is associated with greater WASO during the following week. In short sleepers, mean score of both before and after bedtime HRV predicted 65% of variance in WASO averaged across the next seven nights (R2 = .653, F (1, 5) = 9.41, B = -0.81, p = .28). These effects were not significant in individuals with sleep durations longer than 6 hours. Conclusion In this small sample, short sleepers display a unique, inverse relationship between cardiac vagal activity and sleep continuity, replicating similar findings regarding parasympathetic activity at sleep onset and subsequent WASO. Support (if any) This project is supported by the Psychology Department at the University of Arizona.

Abstract Introduction Insomnia identity is defined as the “conviction that one has insomnia” or self-identifies as an “insomniac.” The present study sought to evaluate whether insomnia chronicity and past insomnia treatment predicts whether someone identifies as an “insomniac” in a large sample of adult participants with sleep complaints. Methods This study utilized a cross-sectional group design in an archival/community dataset that was collected in the Philadelphia area. The data were drawn from 2,950 adults between 18 and 90 years of age who specifically reported problems with total sleep time (TST). All participants answered questions regarding age of problem onset, insomnia treatment history, and insomnia identity (“Do you think of yourself as an ‘insomniac’?”). Results Of the 2,863 participants included in this analysis (Mage=53.5±10.7; 76.6% female; 91.2% White), 45.1% identified as an “insomniac.” Binary logistic regression was used to examine whether TST problem chronicity (Myears=12.3±10.0) and treatment history (36.6%) were associated with the likelihood of identifying as an “insomniac.” Outliers were removed and assumptions were met. The model was statistically significant X2=(2, N=2,863)=172.4, p< 0.001, suggesting that it could distinguish between those who did and did not identify as an “insomniac.” The model correctly classified 60.9% of cases. TST problem chronicity (OR=1.04, 95% CI [1.027, 1.043]) and treatment history (OR=0.51, 95% CI [0.435, 0.596]) significantly contributed to the model. Conclusion Participants were 4% more likely to identify as an “insomniac” with each year of TST problem chronicity and were 49% less likely to identify as an “insomniac” if they engaged in prior insomnia treatment. Findings align with a recent study that also found that individuals with insomnia identity reported longer insomnia chronicity and that individuals with and without insomnia identity seek insomnia treatment. Future analyses should consider additional variables including insomnia frequency (days per week), severity (e.g., total nocturnal wakefulness [TWT]), daytime symptomatology, perceived sleep need, and demographic factors including age, sex, and race. Support (if any) K24AG055602

Purpose of ReviewMental health and sleep disorder symptoms are prevalent in athlete populations, and athletes face unique challenges (e.g., competition pressures and travel demands) that may exacerbate their sleep disorders and symptoms. This review aimed to synthesize the recent literature examining specific sleep and mental health domains in athletes across age, sport, and professional levels. Recent FindingsAthletes commonly experience disturbed sleep, which is related to poorer mental health. Recent literature shows that worse sleep is associated with more depression, anxiety, and stress symptoms in athletes. Sleep disorders and symptoms have been linked to burnout and mood disturbances, with sleep duration and insomnia symptoms specifically linked with suicidal ideation, stress, and burnout. Summary Sleep and mental health of athletes have gained increased clinical and empirical attention, but common limitations across studies (e.g., reliance on cross-sectional data, inconsistent assessment, and definitions of sleep variables) make synthesis difficult. More research is warranted with more precise measurement of heterogenous sleep facets (e.g., duration, timing, and specific disorder symptoms).

Abstract Introduction Sleep is important for athletic and academic performance, injury risk and recovery, and physical and mental health. However, athletes commonly have poor and insufficient sleep, which may be worsened by their inflexible schedules, stress, traveling, and timing of competition. To date, little is known about the relationship between sleep problems and risk for mental health problems in college student athletes. Almost nothing is known about gender, racial and ethnic sleep disparities in this group. The current study aimed to examine the cross-sectional relationships between sleep disorder symptoms and mental health symptoms, further examining differences by gender, race, and ethnicity. Methods Student athletes (N = 1033) from four universities within the Pacific Athletic Conference (PAC-12) were surveyed using previously-validated mental health questionnaires. Since few individuals self-identified as Asian, American Indian/Alaskan Native, Native Hawaiian/Pacific Islander, or “Other,” the race variable was recoded into three groups: White, Black, and Other Underrepresented groups. Gender, race, and ethnicity differences on Athlete Sleep Screening Questionnaire (ASSQ) total scores were examined using a three separate MANOVAs. Next, sleep-disorder symptoms were classified as clinically relevant (n=174) or not (n=733) based on established cutoff values on the ASSQ. Gender and sleep disorder differences on mental health total scores were examined using a MANOVA. Results Women athletes reported significantly worse sleep disorder symptoms as a whole. In addition, Black athletes had worse sleep disorder symptoms. There was a trend for women with sleep problems to have higher PC-PTSD scores than women without sleep problems. In addition, athletes in the Other Underrepresented race group with sleep problems also had greater depression, PTSD, and psychotic symptom severity than White or Black student athletes. There was also a trend for Hispanic athletes with sleep disorder symptoms to have greater ADHD symptom severity. Conclusion To further examine individual differences in specific components of sleep symptoms, sleep duration, insomnia symptoms, medication, quality will be reported in the poster presentation. Future studies are needed to understand whether frequency and chronicity of athletic and external stressors, explain elevations in sleep and other psychiatrics symptoms in student athletes. Support (if any) This project was funded by a PAC-12, Mental Health Coordinating Unit Grant.