667 Effects of Sleep-Extend on glucose metabolism in women with a history of gestational diabetes: A pilot study

Introduction Experimental and epidemiological data have linked insufficient sleep to increased diabetes risk. Women with a history of gestational diabetes (GDM) have a 7-fold greater risk of developing type 2 diabetes. This pilot study explored the feasibility of a sleep extension intervention in women with a history of GDM and short sleep, and the effects on glucose metabolism. Methods Women age 18–45 years with a history of GDM (at least 1 year postpartum) and actigraphy confirmed short sleep duration (<7h/night) on weekdays were randomized at a ratio of 1 control (healthy living information) to 2 cases (6 weeks of “Sleep Extend” intervention: use of a Fitbit, weekly digital content, interactive tools, and coach delivered feedback in order to increase sleep duration). An oral glucose tolerance test (OGTT), 7-day actigraphy recording and questionnaires were obtained at baseline and 6 weeks (at the end of the intervention). Results Twelve women (mean (SD) age 40.3 (4.5) years) participated (n=8 Sleep Extend, n=4 control). Compared to baseline, nightly sleep duration increased in Sleep Extend group (+30.6 (48.8) minutes) but decreased in the control group (-6.8 (22.9) minutes). Both fasting and 2-h glucose levels from OGTT increased in both groups but were greater in the control group (Sleep extend vs. healthy living: fasting glucose +2.1 (9.8) vs. +12.8 (7.3) mg/dL; 2-h glucose +8.2 (21.9) vs. +20.0 (19.4) mg/dL). Self-reported sleep quality improved in both groups. When compared controls, Sleep Extend participants reported improved fatigue symptoms (Promis fatigue score change -5.1 (9.3) vs. 7.0 (1.0), p=0.008), and self-reported physical activity tended to increase (+1614 (3659) vs. -2900 (3922) MET-minutes/week). Combining all participants, an increase in sleep duration correlated with a decrease in fatigue (r= -.62, p=0.04) and anxiety symptoms (r= -.69, p=0.02). Conclusion Sleep extension through coaching and use of remote monitoring is feasible in women with a history of GDM. It appears to decrease fatigue and may improve glucose metabolism and physical activity. Support (if any) NIDDK P30 DK092949

091 The Feasibility of At-home Sleep Extension in Adolescents and Young adults: A Meta-Analysis and Systematic Review

Introduction Insufficient sleep duration has negative consequences for health and performance and is alarmingly common in adolescents and young adults. The primary aim of the meta-analysis and systematic review was to assess whether at-home sleep extension is a feasible means to improve sleep duration and daytime sleepiness without negative consequences for sleep quality or efficiency in adolescents and young adults. An additional aim of the review was to provide a qualitative summary of the health and performance outcomes associated with at-home sleep extension. Methods Peer-reviewed journal articles and doctoral dissertations available in English were searched and screened. Eligible studies had at least five consecutive days of at-home sleep extension, measurement of sleep duration during baseline/habitual sleep and extension of sleep opportunity, and participants 13–30 years of age. Information on primary sleep outcome (i.e., sleep duration), available secondary sleep outcomes (i.e., sleep opportunity, sleep efficiency, sleep quality, daytime sleepiness), and health and performance outcomes were extracted for quantitative synthesis and qualitative review. Results Of the 2254 articles assessed for eligibility, 17 studies (seven in adolescents and ten in young adults) met the eligibility criteria for this review. The average number of days of sleep manipulation was 14.29 (range: 5 to 49 nights). At-home extension of sleep opportunity reliably increased objective (ES = 0.97) and subjective sleep duration (ES = 2.19) and sleep quality (ES = 0.24), and decreased daytime sleepiness (ES = -0.39), when compared to unmanipulated sleep opportunity. Sleep extension was also found to have additional health (e.g., lower psychological stress) and performance benefits (e.g., better athletic performance) across ages and populations. A potential upward publication bias was found based on the distribution of within-subject effect sizes of actigraphic sleep duration. Conclusion The review indicates that at-home sleep extension is feasible in adolescents and young adults to improve sleep duration and daytime sleepiness, and maintain or improve sleep quality. However, the degree of improvement in sleep duration, sleep quality, and daytime sleepiness varied by study population and sleep extension method. Future research should investigate how variations in population and methods of sleep extension impact health and performance outcomes. Support (if any):

613 Variation in Sleep Beliefs and Behaviors Among Caregiver-Child Dyads Participating in a Sleep Extension Intervention

Introduction Given the high prevalence and poor outcomes of insufficient child sleep, effective interventions for the pediatric primary care setting are needed. Collecting family perspectives on intervention strategies is critical to understand and enhance outcomes, particularly among children who do not experience improvements. This study examined variation in qualitative family sleep beliefs and behaviors by quantitative child sleep outcomes of a sleep extension intervention. Methods A total of 24 caregiver-child dyads (child age 9–12 years, M age 11.3 (SD:1.0) years; 46% male; 50% non-Latinx White; 29% Black) participated in a mobile health sleep extension intervention, between March-December 2019, and completed post-intervention semi-structured telephone interviews to solicit intervention perceptions. The intervention used a 25 factorial design consisting of sleep duration goals and weekly performance feedback, with random assignment to sleep health promotion messaging and financial incentive conditions. Sleep duration was assessed via Fitbit Flex 2 devices during 2-week baseline and 7-week intervention periods. We developed a codebook using a grounded theory approach and conducted coding in NVivo. We compared preliminary qualitative themes among children who showed a >=30-minute improvement in sleep duration (‘responders’) versus those who did not (‘non-responders’). Results Of the 24 dyads, 38% (N=9 dyads) were classified as non-responders. Preliminary qualitative themes included: family beliefs about sleep and electronics usage; the study impact on sleep behaviors; and an enhanced awareness of child sleep. Intervention responder and non-responder dyads similarly described family restrictions on evening electronics usage (e.g., electronic parental controls; physical removal of devices) to benefit child sleep. However, more children classified as intervention responders described the benefits of these restrictions and expressed beliefs about the negative impacts of electronics on sleep. Whereas caregivers of both responders and non-responders described efforts to remind their child to go to bed earlier during the intervention, caregivers in the responder group described being more focused on and active in child sleep extension strategies. These included setting bedtime reminders and adjusting family activities to ensure an extended sleep schedule. Conclusion Addressing caregiver-child beliefs about sleep behaviors and engagement in sleep extension strategies could augment sleep extension intervention outcomes in future research. Support (if any) K01HL123612 (JAM), CHOP (JAM), K23HD094905 (AAW)

139 Does Extraversion Predict Subjective Ratings of Sleepiness and Performance During Sleep Deprivation?

Introduction Repeated exposure to total sleep deprivation (TSD) within individuals has demonstrated task-specific, trait-like individual differences in cognitive impairment and subjective sleepiness. Research has suggested that introversion/extraversion may predict individual vulnerability to TSD. While previous analyses have found that extraversion does not reliably predict objective performance impairment on the psychomotor vigilance test (PVT) during TSD, it is not known whether extraversion may predict individuals’ subjective responses to TSD, including subjective ratings of sleepiness, fatigue, mood, performance, and effort. Methods N=21 healthy adults (aged 21-38; 9 women) completed three 4-day/3-night laboratory sessions – each including a baseline night, 36h TSD period, and recovery night –separated by at least 2 weeks each. Two of the sessions were preceded by a week of sleep extension (12h nightly sleep opportunities), while one session was preceded by a week of sleep restriction (6h nightly sleep opportunities), in randomized, counterbalanced order; only the sleep extension sessions are used here. Prior to the experiment, subjects filled out the Eysenck Personality Questionnaire (EPQ), which yielded an extraversion score; one subject did not complete the questionnaire and was excluded from analyses. Every 2h during TSD, subjects completed a 60min neurobehavioral test battery. At the beginning of the test battery, subjects completed the Karolinksa Sleepiness Scale (KSS) and visual analog scales of mood and fatigue (VAS-M and VAS-F). At the end of the test battery, subjects completed self-ratings of their performance (1–7 scale) and effort (1–4 scale). The relationship between extraversion and subjective scores after sleep deprivation (average over last 24h of 36h TSD period) relative to baseline (average over first 12h of TSD period) was analyzed using mixed-effects analysis of covariance, controlling for order, with a random effect over subjects on the intercept. Results No significant relationships were observed between extraversion and subjective estimates of sleepiness (KSS, p=0.45), fatigue (VAS-F, p=0.80), mood (VAS-M, p=0.14), performance (p=0.89), and effort (p=0.93). Conclusion These results indicate that extraversion is not a reliable predictor of trait-like individual differences in subjective vulnerability to 36h TSD. Support (if any) NASA grant NAG9-1161, CDMRP grant W81XWH-20-1-0442

099 A randomized factorial study to understand the components of behavioral sleep extension

Introduction Recent studies have demonstrated that behavioral sleep extension can increase sleep duration among short sleepers. However, little is known about the contribution of the intervention components. The goal of this study is to examine the effects of a fitbit and coaching on sleep extension in a behavioral sleep extension intervention. Methods Participants included adults aged 25 to 65 years with sleep duration <7 hours who were randomized into one of four groups: self-management, Fitbit, coaching, or Fitbit + coaching. The self-management group did not receive any intervention materials. The other three groups received sleep educational materials emailed weekly. The coaching intervention (5-min telephone call) was delivered weekly for 6 weeks to the coaching and Fitbit+coaching groups to enhance motivation. Assessments were completed at baseline, post intervention (6 weeks), and 12-week follow- up. Participants completed self-report questionnaires and actigraphy at study visits. Results were analyzed using mixed models. Results Enrollment and data collection were ended prematurely due to the COVID-19 pandemic. Participants included 32 adults (self-management n=8, coaching n=11, Fitbit n=11, and Fitbit+coaching n=8). Fitbit+coaching group increased hours of sleep by 0.62 h hours more (95% CI: 0.04, 1.20; p=0.047) than the self-management group between their first and second visit. Coaching and the Fitbit groups showed estimated improvements over the self-management group as well: 0.54 h and 0.39 h, respectively, though their differences were not found to be statistically significant (p=0.081 and p=0.20, respectively). At the 12-week follow-up visit, there were no statistically significant differences between groups but the Fitbit+coaching group did maintain their sleep improvement. Conclusion These results suggest that sleep extension intervention components may affect the pattern of sleep changes, but more research is needed to refine and explore changes in sleep with behavioral interventions. Support (if any) R01NR018891