A83 SLEEP, Volume 46, Supplement 1, 2023 understood. New wearable or minimally invasive technologies facilitate the recording of electroencephalography (EEG) with lower spatial resolution than standard EEG but much greater longitudinal dispersion. This enables investigation of day-to- day variation in sleep measured directly with EEG. This study will compare EEG-derived sleep parameters with covariates such as sustained attention and subjective sleep quality. Methods: Twenty-fve healthy adults were implanted with a two-channel subcutaneous EEG (sqEEG) lead. Twenty subjects completed the 1-year protocol (average 32±13 years of age). Their sqEEG signals were recorded each night for 1 year along- side a morning 3-minute Psychomotor Vigilance Task (PVT) and self-reported sleep quality, which included Karolinska Sleepiness Scale (KSS). A deep learning model, U-Sleep, was fne-tuned on sqEEG with synchronized gold standard polysomnography used as ground truth. Hypnograms and sleep parameters were thus automatically calculated. Results: Subjective sleep quality measured by KSS revealed a moderate negative correlation with rapid-eye-movement (REM) duration (r=-0.31, 95% CI=(-0.31, -0.31)), and total sleep time (TST) (r=-0.31, 95% CI=(-0.31, -0.31)). There was a moderate correlation between KSS and mean PVT reaction time (r=0.21, 95% CI=(0.21, 0.22)). There was a low nega- tive correlation between PVT and TST (r=-0.1). Preliminary results indicate a moderate correlation between sleep param- eters and subjective sleep quality. The correlations with PVT were lower, which suggests that 3-minute PVT is not sensitive to TST in normal sleep. However, the correlation between PVT and KSS suggests that PVT does predict subjective sleep qual- ity, but to a smaller degree than standard sleep parameters. Conclusion: Measuring day-to-day variation in high-quality EEG-based sleep recordings has the potential of creating a new branch in sleep medicine. Patients can be evaluated not only by fndings in a single recording but the stability and variation of all fndings can be analyzed. Preliminary results suggest that sub- jective sleep quality can be predicted directly from sqEEG and potentially be explained by behavioral factors in a subsequent cause-effect analysis. Support (if any): The project is supported by Innovation Fund Denmark, UNEEG medical, and T&W Engineering. Abstract citation ID: zsad077.0188 0188 OBJECTIVE AND SUBJECTIVE FEATURES OF SLEEP AND BEHAVIOR IN ADULT SURVIVORS OF PEDIATRIC HODGKIN LYMPHOMA Miguel Navarrete 1 , Daniel Mulrooney 1 , Jamie Flerlage 2 , Melissa Hudson 2 , Belinda Mandrell 2 , Kevin Krull 2 1 St. Jude Children's Research Hospita, 2 St. Jude Children's Research Hospital Introduction: Adult survivors of pediatric Hodgkin Lymphoma (HL) report poor sleep quality and excessive fatigue, often asso- ciated with obstructive sleep apnea (OSA). However, little is known about sleep dynamics in HL survivors without sleep dis- orders. In this exploratory study we evaluated quantitative and qualitative sleep of HL survivors without OSA and examined associations with cancer treatment and functional outcomes. Methods: Adult participants completed two consecutive nights of in-home polysomnography (PSG) with at least 4 hours of recorded sleep. For those participants with a sleep effciency >85% and without OSA (HL survivors N=39, mean[SD] age=35[7.8] years; community controls N=33, age=29[7.4] years) PSG’s were scored following AASM guidelines. Standardized surveys assessed subjective sleep quality (PSQI), fatigue (FACIT-F) and quality-of-life (SF-36). Group differences were evaluated using ANCOVA adjusting for age. Multivariable linear regression was used to evaluate associations between sleep and SF36 measures. Kendall correlations were computed for objective and subjective sleep and chest radiation dosimetry. Results: No signifcative differences between groups were found for standard objective PSG variables (sleep stages, latency, eff- ciency, etc.). Compared to controls, survivors had higher mean heart rate (HR) during sleep (p< 0.003), higher fatigue (p< 0.004), and lower sleep quality (p=0.005). Among HL survivors, poor PSQI ratings were associated with higher HR (τ = 0.36, p=0.001), lower sleep time (τ = -0.26, p=0.019) and increased fatigue (τ = -0.47, p< 0.001). Higher radiation dosimetry was associated with lower PSG sleep effciency (τ = -0.23, p=0.042). Among survivors and community controls, mean HR during sleep (p=0.049) and lower sleep effciency by PSG (p=0.032) were associated with poorer SF36 physical health (p< 0.001). Conclusion: HL Survivors demonstrate elevated mean HR dur- ing sleep, which is associated with patient reported functional limitations. Interventions to lower mean HR during sleep may have the potential to improve sleep quality, physical function, and overall quality of life. Support (if any): NCI - NCI NIH: 1R01CA215405, T32CA225590 Abstract citation ID: zsad077.0189 0189 PARTICULATE MATTER 2.5: EXAMINING HOW ITS EXPOSURE AFFECTS PERCEIVED SLEEP QUALITY AND ELECTROENCEPHALOGRAPHY BASED SLEEP METRICS Kunjoon Byun 1 , Mengjia Tang 1 , Qingyang Liu 1 , Kevin Mazurek 1 , Jovan Pantelic 1 1 Well Living Lab Introduction: Individuals are exposed to various air pollutants every day, both when they are inside and when they are outside. Poor air quality can negatively impact an individual’s sleep qual- ity, especially when the individual has been exposed to Particulate Matter 2.5 (PM2.5) which can adversely affect human health due to its ability to penetrate into a human body easily. Methods: We examined how PM2.5 exposure impacts perceived sleep quality and sleep metrics. 11 individuals (7 Female, 4 Male) with an average age 34.18±10.24 years participated where each participant lived in a simulated one-bedroom apartment unit located in Rochester, MN for 20 nights in total over 4 weeks. Participants wore an EEG-based sleep headband (Dreem) to measure sleep metrics and completed surveys to assess perceived sleep quality and fatigue level each weekday after they woke up. Air quality including PM2.5 was monitored in the units from sensors and from the participants outside the units using a port- able research grade air quality monitor paired with GPS. Total PM2.5 exposure was calculated each day by multiplying the PM2.5 concentration by 6 liter/min of inhalation rate and dura- tion (min). We excluded nights with less than 5 hours of sleep duration. One participant who reported poor sleep quality over 70% of the time was also excluded. A. 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