bluuebunny/crossref_metadata_2025 huggingface dataset:
0270 Obstructive Sleep Apnea detection using ECG morphology and Machine Learning
Authors: Sayeed Fahad , Brooks Justin , Banerjee Nilanjan
Published: May 2023

Introduction

Obstructive Sleep Apnea (OSA) is characterized by reduction in airflow. Hypopnea is a smaller reduction in airflow compared to apnea but also accompanied by drop in oxygen saturation leading to sympathetic activation. Frequent OSA events are correlated with incidence of cardiovascular diseases. Measuring changes in airflow requires overnight polysomnography that is expensive. Electrocardiogram (ECG) is available through wearable devices at home and therefore, detecting OSA events using ECG can make OSA diagnostics more accessible. This paper studies the use of ECG morphology and an ensemble machine learning algorithm to detect OSA events. The algorithm designed is light weight to ensure that it can be implemented on an embedded processor.

Methods

The data from the Apnea, Bariatric surgery, and CPAP (ABC) study provided by the National Sleep Research Resource is used for the analysis. Twenty-six subjects diagnosed with severe OSA are considered with single night polysomnography before treatment. Since OSA events have a minimum 10s duration, 10s non-overlapping windows with OSA labels from a technician scoring is used. Features are extracted from the PQRST complex and majority voting across multiple algorithms is used to select the top 7 features (SDNN, RMSSD, P-P interval, P-duration, P-R interval, T-duration and T-P interval) with highest explanatory power. We train a random forest classifier using leave-one-out methodology where 25 subjects are used for training and the 26th subject is used for testing (all permutations are used). We use sensitivity, precision and F1 score for evaluation.

Results

The algorithm detects the precise occurrence (onset and offset) as well as the total number of events during the night. The precision, sensitivity and F1 scores are 70%, 96% and 80% respectively. The prediction leans towards higher occurrence of OSA events as the subjects suffer from severe OSA. We uncover 32% of false positives are events that are accompanied by significant SPO2 desaturation pointing to the inaccuracy of manual scoring. Assuming these false positives are correct predictions the precision increases to 79%.

Conclusion

The classification of OSA events using ECG features and machine learning demonstrates the feasibility of using wearables to measure OSA in a home setting.

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0276 Detecting Apnea Hypopnea Index for Classified the Severity of Obstructive Sleep Apnea using PPG signals
Authors: Chiu Amy , Shiao Yao , Liu Yu Ting , Lin Chia Mo , Chen Chia Chi
Published: May 2023

Introduction

A polysomnography or home sleep apnea study provides multiple pieces of information to diagnose obstructive sleep apnea (OSA), but the tests are costly with limited access. The study aims to use an automated AHI model with only PPG signals and can be applied to a wearable device.

Methods

We have included patients with different OSA severity to build an algorithm detecting ODI based on the scoring criteria with varying sizes of windows ranging from 10 to 60 seconds. For patients without ODI events, the automated CPC for detecting low-frequency oscillation is included to support the automated AHI model.

Results

The automated ODI and the combination of automated CPC are highly correlated with the AHI. When a CPC is detected without the ODI, the low-frequency coupling can assist in detecting AHI. The accuracy of the automated AHI is 86% compared to the actual AHI, with the sensitivity, specificity and precision at 92%, 73% and 89%, respectively.

Conclusion

The automated AHI algorithm with PPG signals as input can have a high sensitivity and accuracy in screening patients with OSA (AHI≥5), which can considerably be implied in a PPG wearable device.

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0278 Patient Satisfaction with a Novel Sleep Telehealth Platform in the US Military
Authors: Adornetti Julianna , Huang Kathleen , Venezia Janet , Wade Christine , Jones Rachell , Collen Jacob , Capaldi Vincent , Williams Scott , Assefa Samson , Albrecht Jennifer , Wickwire Emerson
Published: May 2023

Introduction

Despite a high prevalence of sleep problems, there is a gross shortage of sleep clinicians within the military health system. To increase access to care, a novel telehealth platform was designed to empower patients and clinicians to make evidence-based sleep treatment decisions. Here, we present initial results from the implementation of this novel sleep telehealth platform in two military treatment facilities (MTFs).

Methods

Participants (N=305) with sleep complaints were recruited remotely and/or in-person from two MTFs. Participants were predominately men (53.4%), who self-identified as White (55.4%) or Black (24.1%) race and were retired or active-duty military servicemembers (69%). Average age was 45.9 (SD=13.3) years. Participants completed a baseline assessment including standardized research questionnaires via a mobile application (WellTap®). During a 10-day remote monitoring assessment, participants completed daily sleep diaries and twice-daily mood/daytime function surveys, while wearing a commercial-off-the-shelf device (Fitbit®). A personalized sleep report including evidence-based treatment recommendations was provided. Participants completed a satisfaction survey regarding the remote monitoring assessment.

Results

The most commonly reported sleep problems were difficulty staying asleep (65%), insufficient sleep (61%), and snoring (40%). Most participants described their sleep quality as “a little good” (51%) or “not at all good” (27%). Thirty-nine percent of participants reported moderate to severe insomnia complaints, 38% reported moderate to severe excessive daytime sleepiness, and 22% reported moderate to severe depressive symptoms. In terms of satisfaction, participants reported the sleep assessments were “easy” to complete (92%) and would consider sleep telehealth approaches in the future (95%). The vast majority of participants preferred to continue with sleep telehealth and preferred to combine a mobile app and remote monitoring with either virtual or face-to-face provider support.

Conclusion

Patients at two military treatment hospitals reported high levels of satisfaction with a novel 10-day intensive remote monitoring assessment including a mobile health assessment and wrist-wearable device. Future studies should evaluate the clinical and economic impact of sleep telehealth and remote monitoring in the U.S. military.

Support (if any)

Department of Defense (W81XWH1990006) via the Medical Technology Enterprise Consortium) to the University of Maryland, Baltimore (PI: EMW).
0287 Humans are capable of achieving sufficient sleep in microgravity
Authors: Flynn-Evans Erin , Glaros Zachary , Jansen Rachel
Published: May 2023

Introduction

Studies consistently find that humans average approximately six hours of sleep per night in space, which is less than they sleep on Earth. It is unclear whether the sleep loss observed during spaceflight is related to modifiable factors, such as irregular scheduling and inadequate sleep environment or due to features of spaceflight that may alter physiology, such as microgravity. Recent missions have afforded crew better, more stable sleep schedules, and an improved sleep environment. Hence, the evaluation of sleep under these improved conditions should provide insight into the causes of sleep deficiency observed in space thus far.

Methods

Crewmembers (n = 19) wore actiwatches for two bouts of data collection lasting two weeks each before flight (at approximately L-270 and L-180), either continuously (n = 9) or for two weeks every two months while in space (n = 10), and for seven days postflight, immediately upon return to Earth. We compared sleep outcomes (sleep duration, wake after sleep onset [WASO], sleep efficiency, and sleep latency) between phases of flight using mixed effects models, with participant and intercept included as a random effect.

Results

Crewmembers averaged 7.3 +/- 0.4 hours of sleep per night in space. Though this was less than they achieved preflight (7.9 +/- 0.5) or postflight (7.8 +/- 0.8; F (2, 2866) = 86.26, p < .0001, ηp2 = .06), this duration of sleep meets the recommended amount for optimal human health and well-being. Sleep efficiency (%) also increased inflight (88.7 +/- 2.7) compared to preflight (84.7 +/- 5.5) and postflight (84.4 +/- 6.1; F (2, 2630) = 124.08, p < .0001, ηp2 = .09), while WASO (min) decreased inflight (29.6 +/- 8.2) compared to preflight (46.4 +/- 15.5) and postflight (48.7 +/- 16.0); F (2, 2629) = 285.90, p < .0001, ηp2 = .18). There were no changes across these variables over time inflight.

Conclusion

We conclude that humans are capable of achieving sufficient sleep in space. Going forward, it is imperative that crewmembers are provided with stable schedules, with moderate workload, and environments that are conducive to sleep.

Support (if any)

NASA Human Factors and Behavioral Health Element, NASA Human Research Program
0294 Sleep Quality Ratings Predict Disagreement Between Daily Objective and Subjective Evaluations of Total Sleep Time in Firefighters
Authors: Marmis Ryan , McGoldrick-Ruth Logan , Kelly Monica , Haynes Patricia
Published: May 2023

Introduction

Accurate sleep assessment in firefighters is necessary to predict risk for error and also to identify sleep disorders like shift work disorder. Unfortunately, the primary tools for sleep assessment, actigraphy and sleep diaries, are sometimes discordant leaving interpretation of sleep unclear. This analysis sought to examine determinants of daily discrepancies between actigraphy and sleep diaries in firefighters to further understand the validity of these measures in an on-call, high risk occupation.

Methods

Daily sleep observations (n = 329) were collected from 60 firefighters over the course of a 6-day recovery period after working 5 non-consecutive 24-hour shifts (5/6 tour). Firefighter subjective sleep was measured using the consensus sleep diary, and objective sleep was measured via wrist-worn actigraphy (Actiwatch-2). Mixed linear modeling (MLM) was employed with purposeful selection of covariates to test which day-level variables (day of recovery, emotional state, sleep quality rating) and person-level variables (burnout, age, gender, minority status) best predicted disagreement in total sleep time (TST) between the sleep diary and actigraphy.

Results

The best-fitting model indicated that TST incongruency (subjective > objective) occurred on days when individuals reported better sleep quality than their own average (Estimate = 19.87, SE = 5.02, p < .001), more feelings of restfulness than their own average (Estimate = 22.70, SE = 4.77, p < .001), less objective TST than their average (Estimate = -.37, SE = .04, p < .001) and for persons with higher duration subjective TST (Estimate = 1.02, SE = .07) and lower duration objective TST (Estimate = -1.00, SE = .08, p < .001). Neither burnout nor daily emotions predicted discrepancies in the two methods of assessment.

Conclusion

Employees most at-risk for potential overestimation of sleep duration were those that habitually received less objective TST or who habitually reported more TST. A discrepancy in measurement occurred on days when employees reported better-than-usual sleep quality and restfulness after sleep. These robust findings demonstrated that positive perception of sleep quality is a potential indicator of sleep overestimation in firefighters. Further research is necessary to examine the short- and long-term consequences of sleep overestimation in this occupation.

Support (if any)

#1R01HL117995-01A1
0295 Association between circadian rhythm of skin temperature and actigraphic sleep in free-living conditions: the HEIJO-KYO study
Authors: Tai Yoshiaki , Obayashi Kenji , Yamagami Yuki , Saeki Keigo
Published: May 2023

Introduction

Skin temperature manipulation with little or no change in core body temperature affects sleep-wake states. However, the significance of 24-hour variation in skin temperature to sleep quality is unclear. We aimed to evaluate whether the circadian rhythm of distal skin temperature (DST) affects sleep quality in free-living conditions and to provide additional evidence about the relationship between thermal input to the skin and sleep-wake states.

Methods

In this cross-sectional study of 2,177 community-dwelling adults (Mean age 68.8 [SD 8.0] years), we measured DST using a temperature logger (Thermochron iButton, Maxim) at 3-minute intervals for seven consecutive days to calculate non-parametric indicators of circadian rhythm: intradaily variability (IV), interdaily stability (IS), and relative amplitude (RA). The participants underwent simultaneous 7-day wrist actigraphy (GT3X-BT, ActiGraph) to measure sleep objectively. The association between non-parametric indicators and 7-day means of sleep measures was evaluated using multivariable linear regression models.

Results

Lower quartiles of IV and higher quartiles of IS and RA were significantly associated with higher sleep efficiency, shorter wake after sleep onset, and longer total sleep time (all p <.001). After adjusting for potential confounders, including physical activity and outdoor temperature, the coefficients for the linear trend of sleep efficiency were −1.07 (95% confidence interval [CI]: −1.45, −0.69), 1.08 (95% CI: 0.75, 1.41), and 1.33 (95% CI: 0.83, 1.84) in IV, IS, and RA, respectively (all p <.001).

Conclusion

Circadian rhythms of DST with higher fluctuation, lower regularity across days, and blunted amplitude were associated with worse sleep quality. Our findings may be applied in thermoregulatory interventions to enhance sleep quality.

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0296 Feasibility of a Sleep Health Intervention Mobile App for Long-Term Sleep Tracking and Usability in Cargo Pilots
Authors: Devine Jaime , Choynowski Jake , Hursh Steven
Published: May 2023

Introduction

Cargo operations frequently require pilots to cross multiple time zones and operate during the night, both of which are fatigue risks. Operators may benefit from an mobile app that provides individual warnings based on prior sleep history. The SleepTank™ mobile app provides warnings about fatigue risk based on the wearer’s individual sleep data as measured by Fitbit devices. In this pilot study, 10 cargo operators provided feedback about the accuracy and usability of the app and up to 100 days of continuous sleep data.

Methods

Ten (N=10) experienced cargo pilots from a major operator volunteered to wear a Fitbit Versa 2 or Charge 5 to evaluate the app over three months (June-October 2022). Pilots completed a Qualtrics online survey about their perceptions of the app’s accuracy and usability in October 2022. Data for up to 100 days was extracted at the end of the study period. Sleep data was summed by date to provide a 24-hour estimate of time in bed (TIB24), total sleep time (TST24), daily sleep intervals (DSI), and average sleep efficiency (SE). Changes to sleep behavior over time were explored using mixed model linear regression.

Results

Cargo pilots (N=10) wore the device with the app for an average of 69±29 days (Range: 25-100 days). Pilots’ TIB24 was 455±111 minutes, TST24 for 400±98 min, and SE was 88±4%. Pilots normally slept once per day (DSI: 1.2±0.5). Pilots’ sleep behavior did not change significantly over the study period (all p> 0.4). All 10 rated the app as being useful, particularly for planning bedtimes and naps. 100% of the pilots rated SleepTank™ as accurate in predicting their fatigue levels.

Conclusion

Experienced cargo operators in this pilot study exhibited healthy sleep habits that were stable over the study period. Operators found the app useful and felt that app-provided fatigue ratings matched their perceived level of fatigue. App usage was not directly related to changes in objective sleep behavior but did validate operators’ experience of fatigue, which may lead to better identification of fatigue symptoms or proactive sleep planning to avoid periods of fatigue risk in the future.

Support (if any)

NA
0299 Sleep Hygiene in Nurses Using Data Collected from Consumer Sleep Technology (CST) with the SleepTank™ mobile app
Authors: Devine Jaime , Choynowski Jake , Hursh Steven , Farag Amany
Published: May 2023

Introduction

Nurses are prone to fatigue and sleep disturbances due to extended hour or shiftwork schedules. Fatigue in nurses not only compromises their own health and safety, but jeopardizes patient care as well. Nurses' work hours and overtime are currently not federally regulated. Fatigue risk mitigation strategies for nurses would benefit from understanding the relationship between sleep duration and work patterns in order to develop tools and regulations to protect against fatigue in the nursing profession.

Methods

Thirteen (N=13) nurses wore a Fitbit Versa 2 and used the SleepTank™ mobile app during a 6-week work rotation. Nurses in this study selected their own work shifts but did have duty-hour limitations. Sleep data was extracted via the mobile app; work schedules were self-reported. Sleep duration data was summed by date to provide a 24-hour estimate of time in bed (TIB24). Repeated measures analysis of variance (rmANOVA) explored differences in TIB24 by number of consecutive days worked and time. Pearson’s correlations evaluated the relationship between work and sleep.

Results

Nurses (N=13) wore the device with the app for an average of 54±15 days. Forty percent (40%) of the study period were work days (22±10). Nurses slept longer on non-work days (525±153 min) compared to work days (481±143; t=3.21, p=0.001). Nurses worked 2 days in a row on average (Range: 1-5). TIB24 was ~30 minutes shorter with each consecutive day worked (F5, 81=8.11, η2=0.08, p=< 0.001). Routinely working a greater number of consecutive days was negatively correlated to TIB24 (r=0.71, p=0.009). Later average work start time was positively correlated with later bedtime (r=0.83, p< 0.001) and shorter TIB24 (r=0.65, p=0.01).

Conclusion

Working more days consecutively and working later shifts was related to shorter sleep duration and later bedtime in nurses. However, given that nurses in this study routinely slept longer than 7 hours per night, poor sleep hygiene does not appear to be an issue in this population. The ability to select their own work shifts may allow nurses to select a schedule that permits healthy sleep behavior.

Support (if any)

N/A
0301 Associations between sleep and cognitive function in patients with Neurological post-acute sequelae of SARS-CoV-2 infection
Authors: Reid Kathryn , Ingram Louis , Jimenez Millenia , Orban Zachary , Zee Phyllis , Koralnik Igor , Maas Mathew
Published: May 2023

Introduction

Fatigue and sleep disturbance are among the most common neurological post-acute sequelae of SARS-CoV-2 infection (Neuro-PASC). We tested the hypothesis that impaired sleep and disrupted rest-activity rhythms are associated with impaired cognitive performance and greater burden of self-reported Neuro-PASC symptoms.

Methods

Forty adults (mean age 45 years, 69% female, 15% hospitalized post COVID) recruited from the Neuro-COVID-19 Clinic at Northwestern Memorial Hospital. Patients were evaluated via clinical assessment/history, chart review, patient reported outcomes for symptom severity (PROMIS cognitive function, fatigue, sleep disturbance, anxiety, depression), cognitive performance tests (NIH Toolbox modules for processing speed, attention, executive function, working memory), and 7 days of wrist actigraphy with a sleep log. We tested associations between objective and subjective measures using Pearson’s correlation and difference from population normative data using Student’s t test.

Results

39 participants provided sufficient data for analysis. The median number of discrete neurologic symptoms attributed to PASC was 6 with 87% reporting ≥4 neurologic symptoms. Fatigue, insomnia, and depression were common (~70% reporting each). Participants reported significant symptom burden in all PROMIS domains, and had measurably worse attention function along with borderline executive function, compared to population normative data (all p< 0.001 except executive cognition p=0.056). Actigraphy showed Neuro-PASC patients had lower sleep efficiency and greater sleep latency (both p< 0.001) but similar wakefulness after sleep onset (WASO) compared to age-matched population controls. Self-reported cognitive symptoms were correlated with severity of fatigue (p< 0.001), anxiety (p=0.05), and depression (p< 0.01), but not subjective sleep disturbance. There were no associations between actigraphy-measured sleep or rest-activity characteristics and self-reported symptoms. Patients with abnormal sleep characteristics (WASO, sleep efficiency, sleep latency) demonstrated impaired objective attention and executive function.

Conclusion

Neuro-PASC patients presenting to a post-COVID clinic have a significant burden of subjective mental health, sleep, fatigue and cognitive impairment symptoms, along with objectively abnormal cognitive performance and sleep, compared to population controls. Interventions to improve sleep could benefit cognitive performance. Prospective studies including larger populations of patients are needed to fully determine the interplay of sleep/circadian rhythm disruption on the cognitive function and quality of life in patients with PASC.

Support (if any)

Center for Circadian and Sleep Medicine
0304 Sleep Quality and Life Satisfaction in Severely Depressed Individuals
Authors: Zahorecz Brianna , Killgore William
Published: May 2023

Introduction

Better sleep quality correlates with higher life satisfaction. However, the strength of this relationship in the severely depressed population has yet to be explored. We hypothesized that better sleep quality would more strongly predict higher life satisfaction in severely depressed individuals, as compared to those who are not severely depressed.

Methods

We conducted a 13 month cross-sectional study on mental health during the main months of the COVID-19 lockdown (April 2020-April 2021). We included the Pittsburgh Sleep Quality Index (PSQI), the Beck Depression Inventory (BDI), and the Satisfaction With Life Scale (SWLS) in months 1 (N=1013), 4 (N=1043), 7 (N=1022), and 10 (N=1011). Using this data, we ran a linear regression between total SWLS scores, which increase with higher life satisfaction, and their responses to item six on the PSQI: “During the past month, how would you rate your sleep quality overall?” which ranged from “very bad” (score of 1) to “very good” (score of 4). The regression was split according to the set BDI score ranges for minimally (N=2178), mildly (N=624), moderately (N=667), and severely (N=609) depressed individuals from the entire sample.

Results

Total SWLS scores were positively correlated with PSQI sleep quality score in minimally, mildly, moderately, and severely depressed groups (Rminimally=0.187, p< 0.001; Rmildly=0.129, p=0.001; Rmoderately=0.180, p< 0.001; Rseverely=0.438, p< 0.001). However, the R2 value was far larger in the severely depressed group than it was in all other groups R2adj/severely=0.191; versus R2adj/minimally=0.034, R2adj/mildly=0.015, and R2adj/moderately=0.031). This suggests that better sleep quality more strongly predicteds higher life satisfaction in severely depressed individuals, compared to those who were not severely depressed.

Conclusion

While poor sleep quality and lower life satisfaction both tend to be associated with more severe depression, these findings suggest the associations between sleep and life satisfaction are far stronger in those who are the most severely depressed. Consequently, these findings suggest that interventions that focus directly on improving sleep quality would likely be a fruitful approach to increase life satisfaction in severely depressed individuals.

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0305 Use of Sleep Medication Increases with Greater Severity of Hazardous Alcohol Use
Authors: Luevanos Maximus , Suckow Emmett , Hildebrand Lindsey , Franca Gabriela , Grandner Michael , Killgore William
Published: May 2023

Introduction

Hazardous alcohol use correlates with greater insomnia, potentially leading to increased use of sleep medications. However, combining sleep medications with alcohol can yield serious and even fatal health consequences. We examined the relationship between alcohol use and sleep medication use during the first year of the COVID-19 pandemic and hypothesized that greater alcohol use would correlate with increased use of sleep medication.

Methods

N=3,930 English-speaking adults from across the U.S. (Mage=37.2, SD=12.4) completed the Pittsburgh Sleep Quality Index (PSQI) and the Alcohol Use Disorders Identification Test (AUDIT) at one of four timepoints (April, July, and October of 2020 and in January of 2021). Individual use of sleep aids was measured by ranking them from 0 (none) to 2 (once or twice a week or more). Additionally, alcohol use was ranked from 0-3 where a higher score indicated higher alcohol dependence.

Results

Alcohol use was modestly but positively correlated with sleep medication use (r = 0.183, p< 0.001). A chi-square analysis showed that the proportion of people reporting heavy use of sleep medications increased among those reporting greater severity of hazardous alcohol use (p< 0.001). Specifically, 21.8% of those in the non-hazardous drinking category reported heavy use of sleep medications, while 25.3% of those in the hazardous drinking group described heavy sleep medication use. For those meeting criteria for probable alcohol dependence, 39.7% reported heavy sleep medication use, and 57.3% meeting criteria for severe alcohol dependence reported heavy sleep medication use.

Conclusion

We found that individuals who scored higher on alcohol use scored higher on sleep medication use, consistent with our hypothesis. These results show that during the COVID-19 pandemic, hazardous alcohol use and sleep medication use were significantly associated. Such findings are concerning, because when combined, alcohol and sleep medications can lead to serious health consequences, including impaired neurocognitive functioning, sleep disruption, parasomnias, risk of overdose, and even death. Given the potential severe risks associated with this behavior, increased clinical assessment, monitoring, and counseling about the dangers of mixing sleep medication with alcohol is warranted. Further work should identify the causal direction and potential mediating factors driving these associations.

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0309 Light exposure before bedtime in pregnancy is associated with a higher risk of gestational diabetes
Authors: Kim Minjee , Facco Francesca , Braun Rosemary , Wolf Michael , Garcia-Canga Blas , Grobman William , Zee Phyllis , Reid Kathryn
Published: May 2023

Introduction

Evidence suggests a link between mistimed light exposure and impaired glucose regulation among nonpregnant adults, yet little is known about the magnitude and significance of evening light exposure during pregnancy. This study examined the relationship between evening light exposure before sleep, during pregnancy, and the incidence of gestational diabetes mellitus (GDM).

Methods

In this prospective cohort study conducted at 8 U.S. clinical sites, nulliparous women wore a wrist actigraphy monitor and completed a daily sleep diary for seven days between 160 and 216 weeks’ gestation. The primary exposure variable was time spent above 10 lux (dim light) during the three hours preceding sleep onset (“TAT10 pre-sleep”), averaged over valid days of recording and categorized to tercile groups (“High”, “Medium”, “Low”). Primary outcome variable was the incidence of GDM. Associations of TAT10 pre-sleep groups with baseline characteristics, sleep and activity variables were examined with Chi-squared and Kruskal-Wallis rank sum tests. Univariate and multiple logistic regression models were used to characterize the relationship between pre-sleep light exposure and GDM.

Results

901 eligible women consented to participate; 758 submitted valid actigraphy recordings. Excluding women with pre-gestational diabetes and those with unknown gestational diabetes status, 741 were included in the analysis. Greater pre-sleep light exposure was associated with an increased risk of GDM (odds ratio [95% confidence interval]: 5.49 [1.8–23.84] in high and 4.05 [1.27–17.94] in medium vs 1 [reference] in low group). Women in the high group had more daytime light and were less likely to be late (midpoint > 5AM) or fragmented (wake after sleep onset ≥ 75th percentile) sleepers. Sleep duration, mean activity level, and light during sleep were not different between groups. After adjusting separately for age, body mass index, race/ethnicity, sleep duration, sleep midpoint, education, employment schedule, season, and daytime light exposure, pre-sleep light exposure remained significantly associated with GDM.

Conclusion

Increased evening light exposure in pregnancy may be an under-recognized, potentially modifiable risk factor for GDM. Future research should investigate light-targeted interventions as a potential preventive strategy for GDM.

Support (if any)

R01HL105549, nuMoM2b Network
0310 Low light exposure during wake is associated with antepartum depressive symptoms
Authors: Warlick Chloe , Kim Minjee , Reid Kathryn , Grobman William , Mercer Brian , Wapner Ronald , Zee Phyllis
Published: May 2023

Introduction

Antepartum depression affects 7-16% of pregnant women and occurs most frequently in the 3rd trimester. Previous studies have demonstrated a relationship between the level and timing of daytime light exposure and depression in non-pregnant populations. However, much less is known about the relationships between light exposure and depression during pregnancy. Thus, in this study, we tested the hypothesis that exposure to less daytime light will be associated with more antepartum depressive symptoms.

Methods

Data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be (nuMoM2b) and the nuMoM2b Sleep Duration and Continuity Substudy were used (N=708). Women enrolled in the substudy wore wrist actigraphy (Spectrum, Phillips Respironics) and completed a sleep log for seven days between 160-230 weeks of gestation. Daytime light exposure was defined as minutes spent at or above the threshold of 50 lux (TAT50) during the wake period, averaged over valid actigraphy recording days. Participants were divided into two groups by TAT50: >25th percentile and ≤25th percentile. The Edinburgh Postnatal Depression Scale (EPDS) was administered during the 1st and 3rd trimesters. A positive screening was defined as an EPDS composite score ≥10 or any endorsement of self-injurious thoughts. Wilcoxon rank sum tests were used to test differences between TAT50 groups.

Results

Low light exposure during the day was associated with antepartum depressive symptoms during the 1st and 3rd trimesters, after adjusting for age, season, race, poverty, sleep midpoint, and sleep duration (OR 1.7, p=0.047). Additionally, later sleep timing, greater day-to-day sleep variability, and increased time spent awake after sleep onset was seen in those exposed to less light (all p< 0.001).

Conclusion

Lower daytime light exposure is associated with poorer sleep quality and may increase risk for antepartum depressive symptoms. These data suggest that in addition to improving sleep quality, promoting daytime light exposure may be a modifiable risk factor for depression during pregnancy.

Support (if any)

Work supported by NHLBI grant R01 HL105549 and NICHD grant U10 HD063036. C.D.W. was funded, in part, by T32HL007909. K.J.R., and P.C.Z. are supported, in part, by R01 HL140580 and P01 AG011412. M.K. is supported, in part, by NIA P30AG059988.
0312 Dynamic Lighting in Memory Care Facilities to Improve Sleep and Mood in Older Adults with Dementia
Authors: Shishegar Nastaran , Alrahyani Mohammed , Yague Ndeye , Fani Mahya
Published: May 2023

Introduction

Of the estimated 5.8 million people in the United States diagnosed with dementia, approximately one-third experience a chronic sleep disorder. Poor sleep quality in older adults is associated with higher levels of depression, anxiety, agitation, and falls as well as lower levels of cognitive and memory performance. Light is the most important environmental element that is not only necessary to fulfill visual tasks but also is the main stimulus that regulates circadian rhythms. This study aims to investigate the effect of a dynamic lighting intervention on sleep quality and mood in older adults with dementia living in long-term care facilities.

Methods

The study is conducted as a within-subject study design over 7 days of conventional lighting (Baseline) followed by 21 days of room lighting (Placebo) and then 21 days of dynamic lighting (Treatment). The dynamic lighting intervention is designed to create an ambient illumination that provides a high level of circadian stimulation in the morning and circadian-neutral lighting in the evenings through delivering varying light intensity and spectrum. The placebo condition exposes participants to a static lighting condition that is equivalent to the average room lighting from morning to evening. The human-lighting interaction occurs in the common areas of the selected memory care facility in Phoenix Greater Area, AZ. Participants of this study are individuals over the age of 65 diagnosed with mild to moderate dementia. Actigraphy is conducted on the last 7 days of each condition to monitor sleep quality. Daily light exposure is tracked continuously throughout the waking day using a non-intrusive wearable sensor. Moreover, caregivers complete two questionnaires (Cornell Scale for Depression in Dementia (CSDD) and Cohen-Mansfield Agitation Inventory (CMAI)) every seven days to assess the short and long-term effects of each condition on mood and agitation.

Results

Findings show notable improvements in mood and nighttime sleep duration and efficiency in participants after exposure to the dynamic condition compared to the baseline and placebo.

Conclusion

The outcomes of this study provide strong evidence that ambient lighting in memory care facilities is a significant contributor to improving sleep and quality of life of older adults with dementia.

Support (if any)

None
0315 Phase shifting in response to light flash sequences during sleep
Authors: Lok Renske , Zeitzer Jamie
Published: May 2023

Introduction

The human circadian clock is synchronized to the outside world through regular ocular light exposure. Light does not need to be continuous to be an effective time cue. Light administered as a sequence of light flashes can be more effective than continuous light of the same illuminance. Exposure to a sequence of light flashes while awake is annoying, but such exposure can occur during sleep, without disrupting sleep, when the circadian system is most sensitive to light. As such, the purpose of this experiment was to determine the magnitude of circadian phase shift that could be obtained when exposing individuals to light flash sequences during sleep.

Methods

Healthy young individuals (n=10) participated in a crossover study of two separate 16-d protocols, the order of which was randomized. The first 14 d were an at-home circadian phase/amplitude optimization period, followed by a 37-h in-laboratory stay during which participants had constant routines (CR) on two consecutive evenings. The onset of salivary melatonin determined during the CRs was used as a marker of circadian phase. Light was dim (< 5 lux) or dark between CRs, except for the light flashes (2-ms flashes, ~1500 lux, every 15 s for 1 h) that were administered via custom goggles while participants slept (starting 30 m after habitual bedtime). During the placebo condition, the goggles provided no light. The impact of the light on circadian phase (pre-post) and polysomnographically-measured sleep were assessed.

Results

As compared to the control condition (12±20 minutes), exposure to the flashes engendered substantial shifts (1.13±1.27 h, p< 0.01; d=1.02). There was considerable variability in response to the stimulus, with two individuals not displaying any phase changes, three having a modest phase change (< 1 hour), two having a moderate phase change (1-2 hours) and two having a large phase shift (>2 hours), one of whom shifted more than 6 hours. There were no significant differences in sleep on placebo or flash exposure nights.

Conclusion

Exposure to a sequence of light flashes during sleep has the capacity to engender large changes in circadian timing without necessitating behavior changes during wake to receive phototherapy.

Support (if any)

Department of Defense (W81XWH-16-1-0223)
0316 A Case Report: Comparing Sleep with and without blue light blockers
Authors: Meghpara Sanket , Awad Dana , Niraula Rijesh , DelRosso Lourdes , Keenan Lynn , Van Gundy Karl
Published: May 2023

Introduction

Blue light filtered eyewear is popular. Studies have looked at the effect of using blue light blockers in the evening but data is lacking on the effect of wearing blue light blockers (BLB) through the day. In this study we compared the effects of wearing BLB all day, on sleep and daytime sleepiness.

Methods

This is a single case report. A 32 year male wore an Actiwatch Pro and collected sleep diaries for 18 days. On days 1-9, non-blue light blocking eyewear (NBLB) were used all day. On days 10-18, BLB were used all day. Karolinska Sleepiness Scale (KSS) and Stanford Sleepiness Scale (SSS) during morning, afternoon and before sleep were recorded. IBM SPSS v. 29.0.0 is used for descriptive analysis, Pearson correlation and T-test comparison.

Results

White light exposure was not statistically different from BLB and NBLB. Data measured in minutes. Mean and standard deviation of in-bed duration with NBLB was 477.89±66.17 and with BLB was 518.06±53.06 (p=0.087). Sleep time with NBLB was 431.89±52.05 and with BLB was 471.72±48.49 (p=0.056). Wake After Sleep Onset (WASO) with NBLB was 43.78±18.63 and with BLB 46.33±14.25 (p=0.374). Percent Sleep with NBLB was 90.96±2.83% and with BLB was 91.09±2.29 (p=0.459). KSS scores for morning on NBLB was 1.75±1.035 and 1.00±0 with BLB (p= 0.023), afternoon NBLB 4.13±1.25, BLB 3.67±0.87 (p=0.194), before sleep NBLB 8.5±1.41 and 8.56±0.53 with BLB (p=0.457). SSS scores for morning on NBLB was 1.63±0.744 and 1.00±0 with BLB (p= 0.012), afternoon NBLB 3.38±1.06, BLB 3.56±0.88 (p=0.353), before sleep NBLB 7.00±1.41 and 7.89±0.60 with BLB (p=0.053). There was a strong correlation between white light exposure and increased WASO (Pearson 0.514) and strong negative correlation (Pearson -0.535) between white light exposure and percent of sleep duration.

Conclusion

Although there were no statistically significant differences between the sleep parameters between NBLB and BLB, we found a strong correlation between sleep time and white light exposure and negative correlation. When using BLB morning alertness was better.

Support (if any)
0321 Manually scoring actigraphy in the absence of a sleep diary: Reliability analysis in Gulf War veterans
Authors: Howell Mary Katherine , Breneman Charity , Allen Nathaniel , Vivino Anthony , Klingaman Elizabeth , Reinhard Matthew
Published: May 2023

Introduction

Sleep disturbance and chronic health conditions are common in military and veteran populations. These individuals may have difficulty accurately and consistently completing sleep diaries, which traditionally have been used as a prominent data input in scoring actigraphy. While actigraphy is considered an objective sleep measure, the process of manually scoring actigraphy data can be subjective, particularly in military/veteran and clinical populations. Few studies utilize and report clear actigraphy scoring guidelines. To promote internal consistency and replicability in actigraphy scoring procedures, this study developed a detailed actigraphy scoring protocol for cases without sleep diary data and performed an inter-rater reliability analysis using a sample of veterans with Gulf War Illness (GWI).

Methods

One hundred fifty-nine nights of actigraphy (Phillips Respironics) data from a random subsample of 25 veterans with GWI were independently, manually scored using the protocol. Mean values for the start and end of rest intervals and derived sleep parameters—time in bed (TIB), total sleep time (TST), and sleep efficiency (SE)—and mean differences were calculated. Inter-rater reliability was evaluated using intra-class correlation (ICC).

Results

No significant differences were observed between manual scorers for mean values for the end of manually scored rest intervals and derived sleep parameters (TIB, TST, and SE). There were significant differences in the start of a rest interval, though the magnitude of the difference (0:06 ± 28) was less than the clinically important 15-minute difference noted in the literature. ICC demonstrated excellent agreement between manual scorers for rest interval start (0.98) and end times (0.99), TIB (0.94), TST (0.98), and SE (0.97).

Conclusion

This protocol may serve as a reproducible set of guidelines for researchers manually scoring actigraphy and enhance internal consistency for studies using this actigraphy scoring software, especially for those working with military populations and clinical populations with significant sleep disturbance and related difficulties yielding quality sleep diary data.

Support (if any)

Veterans Affairs, Office of Research and Development, Clinical Science Research and Development under Merit Review Grant #SPLD-013-13S.
0325 Reducing nocturnal cognitive arousal and rumination mediate treatment effects of digital cognitive behavioral therapy for insomnia
Authors: Iqal Justin , Sagong Chaewon , Kalmbach David , Cheng Philip , Fellman-Couture Cynthia , Ruprich Melissa , Peterson Edward , Roth Thomas , Drake Christopher
Published: May 2023

Introduction

High cognitive arousal—i.e., heightened cognitive activity often in the form of perseverative thinking—is a central feature insomnia. Emerging evidence from randomized controlled trials (RCTs) support alleviating cognitive arousal as a mechanism by which cognitive behavioral therapy for insomnia (CBTI) reduces symptoms of insomnia and depression. However, few RCTs have published CBTI data on cognitive arousal outcomes. The present study tested whether CBTI alleviates cognitive arousal symptoms, and whether reducing cognitive arousal symptoms mediate CBTI effects on insomnia and depression.

Methods

434 patients with clinical insomnia symptoms (Insomnia Severity Index [ISI] ≥ 15) were randomized to digital CBTI or digital sleep education control. Study outcomes included insomnia (ISI), depression (quick inventory of depressive symptomatology self-report 16-item survey, QIDS-SR16), and two indices of cognitive arousal (pre-sleep arousal scale’s cognitive factor [PSASC] and daytime insomnia symptoms response scale [DISRS]). Treatment effects and mediation analyses were conducted using multivariate linear regression.

Results

While controlling for baseline symptoms, CBTI patients, relative to controls, reported lower levels of insomnia (b=-2.45, p<.001), depression (b=-1.13, p<.001), nocturnal cognitive arousal (b=-2.37, p<.001) and insomnia-focused rumination (b=-3.43, p<.001) after treatment. Posthoc independent samples t-tests showed that CBTI produced small-medium effects on nocturnal cognitive arousal (Cohen’s d=.40) and insomnia-focused rumination (Cohen’s d=.31). Mediation analyses showed that reducing nocturnal cognitive arousal (PSASCT2-T1) mediated 29.0% of CBTI effects on insomnia (αβ=-.709, 95%CI=-1.09, -0.38), and 35.1% of CBTI effects on depression (αβ=-.397, 95%CI=-0.65, -0.19). Mediation analyses showed that reducing insomnia-focused rumination (DISRST2-T1) mediated 10.6% of CBTI on insomnia (αβ=-.261, 95%CI=-0.50, -0.08), but did not mediate antidepressant effects (αβ=-.300, 95%CI=-1.46, 0.72).

Conclusion

Digital CBTI effectively alleviated nocturnal cognitive arousal and insomnia-focused rumination. Moreover, alleviating nocturnal cognitive arousal was a mechanism by which CBTI reduced symptoms of insomnia and depression. By comparison, reducing insomnia-focused rumination mediated a small proportion of CBTI effects on insomnia and was uninvolved in depression effects. Although reducing cognitive arousal facilitated insomnia and depression outcomes, CBTI is likely limited by its modest effects on cognitive arousal symptoms. Improving insomnia therapy benefits for cognitive arousal may enhance patient outcomes.

Support (if any)

This RCT (NCT03322774) was supported by NIMH R01-MH122636.
0326 Symptom trajectories in adults with stable heart failure and insomnia: Evidence from a randomized controlled trial of CBT-I
Authors: Conley Samantha , Jeon Sangchoon , Wang Zequan , Tocchi Christine , Linsky Sarah , O'Connell Meghan , Redeker Nancy
Published: May 2023

Introduction

Cognitive behavioral therapy for insomnia (CBT-I) improves individual symptoms of fatigue and daytime sleepiness; however, little is known how CBT-I improves multiple co-occurring daytime symptoms. The purposes of this study are to (1) identify daytime symptom trajectories in adults with insomnia and stable chronic heart failure (HF) over one year, and (2) determine how symptom trajectories vary based on participation in CBT-I or HF self-management education (attention control condition).

Methods

We conducted a retrospective analysis of data from a randomized controlled trial that looked the sustained effects of CBT-I compared to HF self-management over one year (NCT02660385). We measured daytime symptoms (fatigue, depression, anxiety, dyspnea, pain, and daytime sleepiness) at baseline (pre-treatment) and 3, 6 and 12 months. Symptom cluster trajectory were identified using group-based trajectory modeling, and the association with CBT-I was examined using logistic regressions.

Results

We randomized 175 participants (M age=63.0 (12.9) years, 57% men). We found four daytime symptom cluster trajectories, (A) low improving symptoms (N = 67, 38.3%); (B) low psychological symptoms and high improving physical symptoms (N = 40, 22.8%); (C), high improving symptoms (N = 42, 24.0%); and (D) high not improving symptoms (N = 26, 14.9%). There was no statistically significant difference between CBT-I and HF self-management across 4 symptom trajectories (p = .2509). However, people who received CBT-I vs. HF self-management education had higher odds of belonging to trajectory A compared to B/C/D after controlling for baseline fatigue (OR = 3.27 95% Confidence Intervals [1.39, 7.68]).

Conclusion

Most participants experienced improved daytime symptom profiles over 12 months after both interventions. Participants in the CBT-I group experienced symptom improvement even when they started with low symptoms.

Support (if any)

National Institute of Nursing Research [R01NR016191 and P20NR014126].