Abstract Introduction Despite being used and widely recommended since the 1970s, few studies have examined whether adherence to sleep hygiene practices affect objectively measured sleep in non-clinical populations. While individual components of sleep hygiene such as limiting caffeine and alcohol consumption are clearly related to sleep by plausible physiological and psychosocial mechanisms, the real-world evidence of overall sleep hygiene practices on sleep is surprisingly inconsistent. Here, we examined the association between self-reported sleep hygiene practices and objectively measured sleep in a general population. Methods Responses to a survey on sleep hygiene were used and matched with objective sleep data, resulting in data from 720 users (mean age: 52.5 ± 15.9, 63.4% female). Objective sleep data across 92,808 nights were included in the analysis from the PSG-validated SleepScore Mobile Application, which uses a non-contact sonar-based method to capture sleep-related metrics and self-reported lifestyle. Self-reported sleep hygiene practices were assessed with 13-items on a 5-point scale ranging from “Never” to “Always”. Descriptive statistics and linear regressions were used for the analysis, controlling for age and gender. Results Overall, the top three most frequented poor sleep hygiene practices were going to bed at different times (29.7%), overthinking/worrying in bed (24.0%), and waking at different times (22.7%). Linear regressions revealed a significant negative association between composite sleep hygiene scores and objective sleep measures, whereby poorer sleep hygiene was associated with significant reductions in total sleep time (ß=-0.89, SE=0.33, p< 0.01), REM duration (ß=-0.24, SE=0.10, p< 0.05), and SleepScore (ß=-0.15, SE=0.05, p< 0.01), an objective sleep quality metric. No significant associations were observed between individual sleep hygiene factors and objectively measured sleep. Conclusion While we could not identify a relationship between individual hygiene factors and sleep, poorer aggregated sleep hygiene scores were associated with poorer objectively measured sleep. Thus, sleep health may not be defined by one single behavior, but rather by the sum of its parts. Future work should examine the efficacy of personalized sleep hygiene factors in sub-clinical populations, where targeted sleep hygiene education may be preferred given it is more intuitive and less burdensome than other behavioral interventions. Support (if any) SleepScore Labs

Abstract Introduction Insomnia is highly prevalent, but difficult to diagnose due to night-to-night sleep variability, and difficult to treat in part due to the lack of trained providers. Consumer sleep technology (CST) allows for longitudinal sleep monitoring in a natural environment. One such device, the Sleep Score Max (SleepScore Labs) combines longitudinal non-contact sleep tracking via radio-frequency biomotion sensor technology with an individualized sleep guide function, which provides tailored recommendations to improve sleep based on the user’s objectively measured sleep, bedroom environment, and self-reported daily activity. Focusing on sleep efficiency, we examined if level of engagement with the sleep guide is related to sleep improvement in chronic insomnia. Methods As part of a larger study, N=30 individuals with chronic insomnia (ages 38.7±14.9; 8 male) participated in an 8-week at-home sleep monitoring study. Enrollment criteria included being 18-65y old and meeting ICSD-3 criteria for chronic insomnia with no other clinically relevant condition contributing to sleep disturbance. Participants were instructed to track their sleep with the aforementioned device and engage with the sleep guide daily for 8 consecutive weeks. For observed sleep efficiency, means (characterizing sleep overall) and within-subject standard deviations (characterizing ­night-to-night variability) were assessed by week, and change scores from week 1 to week 8 were correlated with level of engagement with the sleep guide function (defined as percentage of days interacted with the guide within the 8-week period). Results From week 1 to week 8, mean sleep efficiency increased non-significantly (t[23]=0.83, p=0.42), but night-to-night variability decreased significantly (t[23]=-2.16, p=0.041). There was no significant relationship between change in mean sleep efficiency and level of engagement with the sleep guide (r=0.005, p=0.980), but there was a significant relationship between change in night-to-night variability and level of engagement (r=-0.48, p=0.018), such that night-to-night ­variability decreased more in those with greater engagement. Conclusion Night-to-night sleep variability is a chief complaint of people with chronic insomnia, and reduced night-to-night variability in sleep efficiency may indicate improvement. As such, engagement with an individualized sleep guide incorporated in a CST device may help individuals with chronic insomnia improve their sleep. Support (if any) NIH grant KL2TR002317; research devices provided by SleepScore Labs.

Abstract Introduction While it is well known that ocular light strongly entrains the central circadian clock, there has been a lack of evidence-based recommendations for daytime, evening, and nighttime light exposure for healthy adults. Recently, an expert consensus report with recommendations for light exposure has been published. Although the general population is likely not yet acquainted with these recommendations, we examined the self-reported practice of daily and nightly light exposure based on these published guidelines. Methods Online survey data were collected from 168 participants through convenience sampling (age range: 25-80, mean age: 58.9 + 12.6 years, 55% female). The practice of expert, consensus-based light recommendations was assessed with 4 items on an 8-point frequency scale ranging from “Never” to “Every day of the week”. The scale covered 1) indoor daytime light recommendations, 2) indoor evening light recommendations, 3) nighttime light recommendations for the sleep environment, and 4) an additional morning-specific question. Results Within the first hour after waking up, the majority of respondents (53%) reported never receiving exposure to at least 15 minutes of sunlight or simulated bright light, with only 16% reporting exposure every day of the week. Throughout the daytime, 54% of respondents reported receiving consistent natural indoor light or sunlight every day of the week, and 8% reported never receiving any. During the evening, 47% of respondents reported never dimming lights and avoiding bright light starting at least 3 hours before bedtime. However, 24% reported avoiding bright light sources before bedtime every day of the week. While sleeping, nearly 80% of respondents reported keeping their bedroom as dark as possible, no brighter than natural moonlight, every day of the week, with only 10% reporting never doing so. Conclusion Self-reported practice of recently published light recommendations was poor for both morning and evening light exposure. These findings suggest that sleep and circadian health campaigns should focus on the importance of bright light in the morning upon awakening, and of dimmed light in the late evening before bedtime. Support (if any) SleepScore Labs

Background: Accurately and unobtrusively testing the effects of snoring and sleep interventions at home has become possible with recent advances in digital measurement technologies. Objective: The aim of this study was to examine the effectiveness of using an adjustable bed base to sleep with the upper body in an inclined position to reduce snoring and improve sleep, measured at home using commercially available trackers. Methods: Self-reported snorers (N=25) monitored their snoring and sleep nightly and completed questionnaires daily for 8 weeks. They slept flat for the first 4 weeks, then used an adjustable bed base to sleep with the upper body at a 12-degree incline for the next 4 weeks. Results: Over 1000 nights of data were analyzed. Objective snoring data showed a 7% relative reduction in snoring duration (P=.001) in the inclined position. Objective sleep data showed 4% fewer awakenings (P=.04) and a 5% increase in the proportion of time spent in deep sleep (P=.02) in the inclined position. Consistent with these objective findings, snoring and sleep measured by self-report improved. Conclusions: New measurement technologies allow intervention studies to be conducted in the comfort of research participants' own bedrooms. This study showed that sleeping at an incline has potential as a nonobtrusive means of reducing snoring and improving sleep in a nonclinical snoring population.

Abstract Introduction Exercise is bidirectionally associated with sleep, whereby exercise can be an efficacious element of behavioural therapy for sleep, and longer sleep duration has been associated with increased physical activity. Given poor sleep and physical inactivity are each widely recognized as critical public health priorities, further research into the relationship between objective sleep and indices of exercise using ecologically-valid sleep measurement tools is warranted. Here, we examined the association between self-reported exercise intensity and duration, and objectively measured sleep using consumer sleep technology. Methods Data from 2,662 users (mean age: 47.4, 36.5% female) across 343,308 nights were included in the analysis from the PSG-validated SleepScore Mobile Application, which uses a non-contact, sonar-based method to objectively capture sleep-related metrics, and questionnaires to capture self-reported data. Exercise intensity ( “At what level of intensity do you work out?”, 3 point scale) and exercise frequency (“How many times a week do you exercise for at least 20 mins?”, 5 point scale) were gathered used self-report questionnaires. Linear regression modelling was used for analysis, with age and gender used as confounding variables. Results Greater reported exercise frequency was associated with an increase in TST (ß=3.3 mins, SE=0.838, p<0.001) and sleep efficiency (ß=0.5%, SE=0.116, p<0.001). Exercise frequency was also associated with reductions in WASO (ß=-1.153mins, SE=0.429, p<0.01) and SOL (ß=-0.425mins, SE=0.163, p<0.01). Greater reported exercise intensity was associated with an increase in TST (ß=4.908 mins, SE=1.886, p<0.01) and sleep efficiency (ß=1.16%, SE=0.255, p<0.001). Exercise intensity was also associated with reductions in WASO (ß=-3.282mins, SE=0.965, p<0.01) and SOL (ß=-0.852mins, SE=0.272, p<0.01). Conclusion Self-reported exercise frequency and intensity were associated with improved objective sleep metrics across the board. This big data finding using ecologically-valid consumer sleep technology can further contribute to public health recommendations regarding the positive impact of exercise on sleep. Support (If Any)

Abstract Introduction Circadian rhythms progressively delay throughout adolescence until older adulthood when they advance to become early as children. Chronotype represents a subjective assessment of when one feels their performance is optimal. Evidence suggests evening chronotype is associated with adverse health effects. Presently, it is unclear whether sleep-wake cycles and chronotype diverge across ages. Here, we examined whether self-reported chronotype was associated with the daily start of the sleep-wake cycle (indicated through objectively measured bedtime) across the lifespan using a large, ecologically-valid dataset. Methods Data from 11,026 users (mean age: 45.3, 54.3% female) across 1,167,489 nights were included in the analysis from the PSG-validated SleepScore Mobile Application, which uses a non-contact, sonar-based method to objectively capture sleep-related metrics, and questionnaires to assess self-reported lifestyle factors. Chronotype was subjectively assessed with a 5-item question ranging from definitely morning-type to definitely evening-type. Bedtime, a proxy for the daily start of the sleep-wake cycle, was captured as the time at which users started a sleep recording in the Application. Linear regressions were used for the analysis. Results Overall, chronotypes showed a near-normal distribution with a skew toward definite evening types (n=2147) compared to definite morning (n=1560) types (21.70% versus 15.70%). As expected, average bedtime was earliest for definite morning types (mean=23:02 ± 86.4 mins) and latest for definite evening types (mean=01:10 ± 102 mins). Across all chronotypes, linear regressions revealed a significant negative association between overall age and bedtime (p<0.0001). Among definitive evening types, younger ages had later bedtimes and older ages had earlier bedtimes (ß=-0.014, SE=0.002, p<0.00001). Further, the degree of change in bedtimes across age was largest for definite morning types, whereby average bedtime decreased from 23:38 (SD=86.3 mins) at age 20 to 22:29 (SD=88.2mins) at age 80 (ß=-0.019, SE = 0.002, p<0.00001). Conclusion The present analysis showed that, across chronotypes, younger ages had later bedtimes and older ages had earlier bedtimes, presumably driven by age-related changes in circadian rhythmicity. This association was also exemplified by morning-types showing the greatest change in bedtimes across the lifespan. Future prospective studies are warranted to examine the relationship between longitudinal changes to chronotype and endogenous circadian rhythmicity across the lifespan. Support (If Any)

Abstract Introduction Individuals with insomnia report poor sleep quality and non-restorative sleep, and often exhibit irregular sleep patterns over time. First night effects and logistical challenges make it difficult to accurately measure these sleep characteristics in the laboratory. Also, sensitivity to sleep disruption from obtrusive devices confounds sleep measurements in people with insomnia in their naturalistic setting. Non-contact devices (NCDs) may address these issues and enable ecologically valid, longitudinal and unobtrusive characterization of sleep in individuals with insomnia. We present results from a NCD, previously validated against polysomnography, – SleepScore Max (SleepScore Labs) – assessing the sleep of individuals with chronic insomnia, compared to healthy sleeper controls, in their home setting. Methods A total of 112 individuals participated in an at-home sleep monitoring study including 83 with chronic insomnia (ages 19-65, 58 females) and 29 healthy sleeper controls (ages 19-54, 21 females). Enrollment criteria included being 18-65 years of age and, for the insomnia group, meeting International Classification of Sleep Disorders (3rd edition; ICSD-3) criteria for chronic insomnia with no other clinically relevant condition contributing to sleep disturbance. Participants used the NCD to record their sleep periods each night for 8 weeks. Sleep measurements were analyzed for group differences in both means (characterizing sleep overall) and within-subject standard deviations (characterizing night-to-night sleep variability), using mixed-effects regression controlling for systematic between-subject differences. Results On average, individuals with chronic insomnia exhibited increased total wake time, wake after sleep onset, and decreased sleep efficiency relative to healthy sleeper controls (F>6.8, p<0.01). Additionally, they demonstrated greater night-to-night variability in time in bed, total sleep time, sleep latency, total wake time, wakefulness after sleep onset, sleep interruptions, sleep efficiency, and light and deep sleep (F>4.4, p<0.05). Conclusion In our sample of individuals with chronic insomnia, a NCD naturalistically detected differences from healthy sleeper controls in multiple sleep parameters, both on average and in terms of night-to-night variability. Capturing night-to-night variability in the home setting adds an important dimension to our understanding of poor sleep and provides a more comprehensive, ecologically valid characterization of chronic insomnia as experienced in daily life. Support (If Any) NIH grant KL2TR002317; research devices provided by SleepScore Labs

Abstract Introduction This study examined if a diffused fragrance used at home before bedtime would contribute to sleep improvement in a sample of healthy females. Existing evidence regarding the sleep-promoting properties of fragrances often has been anecdotal or based on clinical research, thereby limiting the generalizability and ecological validity of results. Methods 26 women with self-reported interest in air care to support healthy sleep participated in a 9-week field study. A within-subjects, counterbalanced intervention design was implemented, comparing 3 weeks of nightly product use to 3 weeks without using the product after a baseline period. Intervention consisted of the use of a fragrance diffuser and fragrance by Reckitt’s Scientific Platform Fragrance Research for at least an hour at the participants’ preferred settings in the room where they spent the most time before going to bed. Sleep was measured objectively with SleepScore Max every night. Self-report data were collected at bedtime, in the morning, and after each measurement period. Multilevel regression and paired t-tests were used to test for statistical significance. Results Across all participants there were 835 nights of tracked sleep. Participants (100% female, age 21 to 55, average 36 years old) showed improvement in both objective and perceived sleep during the intervention. Participants got more deep sleep, spent a greater proportion of the night in deep sleep, and had an improved BodyScore, a measure of deep sleep (ps<.05). Additional objective improvements were related to sleep consistency: fewer awakenings during the night, less time awake during the night, and better sleep maintenance (ps<.05). Self-report results complemented the objective findings. Participants felt sleepier at bedtime, felt they woke up less often and spent less time awake after initially falling asleep, reported better sleep quality, and experienced better mood both at bedtime and in the morning (ps<.05). No significant negative impacts were seen on sleep in the objective and self-report measures. Conclusion Using the fragrance diffuser before bed may contribute to improvement of many aspects of sleep within this study population of females without underlying sleep conditions. Objectively improved sleep outcomes were supported by self-report, showing multifaceted benefits of the diffused fragrance on sleep. Support (If Any) Reckitt

Abstract Introduction With the rise of sleep measurement technology becoming widely available to the public, it has become apparent that traditional sleep metrics might not be best suited for a lay audience. Most consumer industry has started including a metric that would capture sleep quality, although the exact calculations of these scores remain proprietary. These novel outcome metrics require a set of reference values in order to become interpretable. Here, we provide reference values for the parameters SleepScore, BodyScore and MindScore as included in the SleepScore Labs non-contact radiofrequency sleep measurement devices. Methods SleepScore is a sleep quality metric that includes objectively measured total sleep time (TST), sleep onset latency (SOL) and sleep stage durations, normalized for aged and sex, using reference values of Ohayon et al (2004), ranging from 0-100. BodyScore reflects the normalized amount of deep sleep, whereas MindScore reflects the normalized amount of REM, ranging from 0-100. Data from 40,862 S+ and Max users between 18 and 98 years old were used to calculate distribution statistics. Results The average age of users was 53±15 years old. Individual scores of SleepScore, BodyScore and MindScore ranged from 0-100 and their distribution was left-skewed. SleepScore averaged 81±11, with the first quartile (Q1) at 73, median at 81 and third quartile (Q3) at 88, and a mode of 89. BodyScore averaged 81±10 with Q1 at 73, median at 80 and Q3 at 86, and a mode of 84. MindScore averaged 78±10 with Q1 at 72, median at 79 and Q3 at 84, and a mode of 83. Despite being algorithmically normalized for age, average SleepScore increased from 70 to 88 across the age range, BodyScore increased from 71 to 89, and MindScore increased from 75 to 81. Conclusion SleepScores, BodyScores and MindScores presented to the average consumer will mostly show them a number in the low 70 to high 80 range. This distribution was intentionally created as being left-skewed to prevent triggering anxiety that may contribute to orthosomnia. Despite the intent to create a normalized score that would not be impacted by age, the data show an increase of scores by age. Support (If Any)

Abstract Introduction Non-contact devices (NCDs) have been developed to measure sleep longitudinally and unobtrusively in the naturalistic home setting. We compared longitudinal measurements from a wrist actigraph (Actiwatch-2, Philips Respironics) and from a NCD (SleepScore Max, SleepScore Labs) in a sample of adults with insomnia Methods N=71 adults (ages 39.0±13.0y; 50 women) who met ICSD-3 criteria for chronic insomnia and were otherwise healthy participated in an at-home sleep monitoring study. Participants continuously wore the actigraph for one week, then used the NCD to record only nightly sleep periods for the next 8 weeks. Week-by-week within-subject averages and standard deviations (SDs) over days were assessed for five major sleep parameters: total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), time in bed (TIB), and sleep efficiency (SE). These sleep parameters were analyzed with mixed-effects ANOVA comparing week one (actigraphy) to the next 8 weeks (NCD), and correlations between the first week (actigraphy) and second week (NCD) were calculated. Results Significant differences for actigraphy versus NCD were found for the weekly averages of SOL and WASO (F>25.8, p<0.001). The NCD measured longer average SOL (M±SEM=25.0±1.5min) than actigraphy (12.6±2.0min) and less average WASO (40.5±2.7min) than actigraphy (51.1±3.2min). Further, significant differences were found for the weekly within-subject SDs of TST and WASO (F>7.52, p<0.01). The NCD measured greater SD for TST (71.6±2.8min) than actigraphy (60.0±4.7min) and greater SD for WASO (25.0±1.4min) than actigraphy (19.2±2.2min). Actigraphy and NCD weekly averages were positively correlated for TST, WASO, TIB, and SE (p<0.001), but not SOL (r=0.03, p=0.80). Similarly, weekly within-subject SDs were positively correlated for TST, WASO, TIB, and SE (p≤0.05), but not SOL (r=–0.03, p=0.81). Conclusion Actigraphy and NCD were not used simultaneously, precluding a direct comparison between these measurement modalities. Nonetheless, in this ecologically valid context, significant differences were only found for the weekly averages of SOL and WASO and for the weekly within-subject variability of SOL and TST, with significant correlations between the devices for all variables except SOL. Although actigraphy tends to underestimate SOL, NCD validation against polysomnography in chronic insomnia is warranted. Support (If Any) NIH grant KL2TR002317; research devices provided by SleepScore Labs.

Abstract Introduction Changes in social zeitgebers across the lifespan likely impact the interplay between biological and social clocks that fosters the circadian misalignment seen in social jetlag. Extant literature is limited to self-reported methods and cross-sectional designs and suggests older adulthood may be associated with a reduction in social jetlag given declining social obligations occurring after retirement. Using longitudinal ecologically-valid data, we examined the association between age as a continuous measure and social jetlag. We also examined whether work cessation is associated with a reduction in social jetlag. Methods Data from 2,446 users (mean age: 52.2 +/- 15.8, 51.8% female) across 473,113 nights were included in the analysis from the PSG-validated SleepScore Mobile Application, which uses a non-contact sonar-based method to objectively capture sleep-related metrics and self-reported lifestyle. Social jetlag (expressed in minutes) was defined as the difference between midsleep times on week and weekend days from a user’s total recording period. Linear regressions were used for the analysis. Age was examined as a both continuous variable, and as a dummy variable in a subsequent analyses in a subgroup of older adults, serving as a proxy for pre– (n = 604, age: 54-64, mean age: 60.5 +/- 2.8) and post-retirement (n = 428, age: 65-75, mean age: 69.9 +/- 2.8). Results Linear regressions revealed a significant negative association between overall age and social jetlag, whereby older age was associated with a reduction in social jetlag (ß=-0.64, SE=0.082, p<0.0001). In agreement with this finding, post-retirement age was associated with a significant reduction in social jetlag (ß=-15.31, SE=3.78, p<0.0001) as compared to pre-retirement. Conclusion The present analysis showed that social jetlag decreases across the lifespan, and its reduction appears to be amplified following retirement. Our findings are in-line with prior work demonstrating the reduction, but not extinction, of social jetlag in older adulthood. Support (If Any)

Abstract Introduction Women are more likely to report sleep difficulties. There are many ways for sleep to be disrupted (social influences, external sensory stimuli, somatic cues) but limited data exists on the relative burden of these disruptors. The purpose of the present analysis was to compare the occurrence of disruptors between males and females of equal ages. Methods We used self-reported data from the PSG-validated SleepScore mobile app to analyze the relative occurrence of a variety of sleep disruptors in an age- and gender-balanced sample of users (39,560 male and 39,560 female, median age=41, SD=15). Fisher's exact test was used to examine whether gender was a significant factor in the likelihood of reporting each disruptor. Disruptors were categorized as follows: somatic cues included hot flashes/thermal discomfort, chronic pain, bathroom visits, and heartburn; external sensory stimuli included temperature, light, and noise; social influences included: bed partner, pet, and children. All p < 0.00001 results are reported. Results Women were significantly more likely than males to report at least one sleep disruptor (Odds ratio (OR): 2.29, prevalence: 90% vs 80%) and were more likely to report sleep disruption attributed to external stimuli (OR: 1.61, prevalence: 54% vs 42%), somatic cues (OR: 1.66, prevalence: 68% vs 56%), and social influences (OR: 1.95, prevalence: 50% vs 34%). Among somatic cues, women were more likely than males to report hot flashes/thermal discomfort (OR: 3.42), chronic pain (OR: 2.32), bathroom visits (OR: 1.24), and heartburn (OR: 1.14). Among external sensory stimuli, women were more likely than males to report sleep disruption attributed to sound (OR: 1.58), light (OR: 1.54), and temperature (OR: 1.54). Among social influences related disruptive factors, women were more likely than males to report sleep disruption attributed to pets (OR: 2.31), bed partners (OR: 1.65), and children (OR: 1.62). Conclusion The present analysis found that women reported higher rates of regular disruption for every cause. These findings highlight the role of gender in sleep-health reporting behavior and mirror other findings showing lower symptom reporting and healthcare utilization among males. Support (If Any)