Non-school Night Research Articles

The relationship between modifiable lifestyle behaviours and self-reported health in children and adolescents in the United Kingdom.

Obesity, along with its associated health issues, is closely tied to lifestyle habits. While certain elements affecting childhood health, such as genetics and ethnicity, are beyond individuals' control, there exists modifiable lifestyle behaviours that can facilitate healthier living. This study employed multiple regression analysis to investigate the relationship between specific modifiable lifestyle behaviours and self-reported health. The independent variables considered included days of moderate to vigorous physical activity (MVPA), fruit and vegetable consumption, breakfast frequency, school night sleep duration, and non-school night sleep duration. These variables were chosen for their practical modifiability within participants' daily lives. The analysis revealed a highly significant overall model (F(13,11363) = 191.117, p < .001), explaining 17.9% of the variance in self-reported health. Notably, higher MVPA levels were associated with improved self-reported health (B = 0.136 to 0.730, p < .001). Additionally, regular breakfast consumption and increased fruit and vegetable intake exhibited positive associations with self-reported health (B = 0.113 to 0.377, p < .001), while girls reported lower self-reported health (B = -0.079, p < .001). School night sleep duration was positively linked to self-reported health (B = 0.071, p < .001). Furthermore, a dose-response relationship between MVPA, dietary habits, and health was identified. These findings hold substantial potential for public health campaigns to promote healthy behaviours and prevent chronic diseases in young individuals. It is imperative to emphasise that all the variables considered in this study are readily modifiable aspects of individuals' lives, offering a promising avenue for personal health and well-being enhancement.

0222 University students procrastinate going to bed for multiple reasons that result in shorter sleep on school and non-school nights

Abstract Introduction Bedtime procrastination occurs when individuals go to bed later than they intended despite knowing that there may be negative consequences. The prevalence, drivers, and consequences of bedtime procrastination on daily sleep behavior are poorly understood. In university students, (1) we measured the frequencies and reasons for going to bed later than intended, and (2) we tested the hypothesis that bedtime procrastination results in shorter sleep on school nights but not non-school nights. Methods University students living in a residential college (n=109) completed daily sleep diaries for 2 weeks during their school semester. Each morning, students indicated if they went to bed later than they intended and the associated reasons. Sleep parameters included bedtime, wake-up time, and nocturnal time in bed for sleep. Simple effect sizes were used to compare mean differences in sleep behavior on nights with versus without bedtime procrastination. Results Students went to bed later than they intended on approximately half of all nights, irrespective of whether they had school the next day (school nights, 56.6%; non-school nights, 51.6%). On school and non-school nights with bedtime procrastination, time in bed was shorter by about 45 min (school nights: mean difference=-0.74h, 95% CI= -0.97h to -0.53h; non-school nights: mean difference=-0.79h, 95% CI=-1.01h to -0.58h). The most common reason for bedtime procrastination was “lost track of time” (34.9%), which was reported nearly twice as often compared with “wanted some me time” (19.5%), “wasn’t tired yet” (16.7%), “engaging in social leisure activities in person” (15.2%), and “engaging in social leisure activities online” (13.6%). The largest decrease in time in bed was observed when bedtime procrastination occurred due to in-person social activities (mean difference=-1.23h, 95% CI=-1.49h to -0.97h). Conclusion University students living in a residential college frequently engaged in bedtime procrastination on school and non-school nights, resulting in shorter nocturnal sleep opportunities. Losing track of time was the most common reason, suggesting that interventions for improving sleep in these students should emphasize self-regulation and time management skills. Our findings also suggest a strong social component of bedtime procrastination. Support (if any) Ministry of Education, Singapore (MOE2019-T2-074)

Delayed bedtime on non-school days associates with higher weight and waist circumference in children: Cross-sectional and longitudinal analyses with Mendelian randomisation.

Sleep duration has been linked with obesity in population-based studies. Less is known about bedtimes and, especially, if discrepancy between bedtimes on school and non-school days associate with adiposity in children. The associations of self-reported bedtimes with the body mass index z-score (BMIz) and waist-to-height ratio (WtHr) were examined among children with a mean (SD) age of 11.2 (0.85) years in cross-sectional (n=10,245) and longitudinal (n=5085) study settings. The causal relationship of whether BMIz contributes to bedtimes, was further examined in a subset of 1064 participants by exploiting Mendelian randomisation (MR). After adjusting for sleep duration and other confounders, every 0.5 h later bedtime on non-school nights and a delay in bedtime in non-school nights compared with school nights associated with 0.048 (95% CI 0.027; 0.069) and 0.08 (95% CI 0.056; 0.105) higher BMIz as well as 0.001 (95% CI 0; 0.002) and 0.004 (95% CI 0.003; 0.005) with higher WtHr, respectively. Moreover, every 0.5-h delay in bedtime in non-school nights compared with school nights associated with 0.001 (95% CI 0; 0.002) greater increase in WtHr in the 2.5 years follow-up. Thus, a 2-h delay in bedtime at the age of 11 years corresponds with a 0.6 cm increase in waist circumference. The MR analysis did not indicate an opposite causal relationship: higher BMIz was not causing delayed bedtimes. Later bedtime on non-school days and discrepancy in bedtimes associated with increased BMIz and WtHr, while longitudinally these predicted higher WtHr, independently of sleep duration. Promoting early bedtimes, especially on weekends, should be considered in obesity prevention among school-aged children.

Clinical Research Updates.

Clinical Research Updates.

Bedtime procrastination and chronotype differentially predict adolescent sleep on school nights and non-school nights

ObjectivesBedtime procrastination (BTP) refers to the tendency to delay sleep beyond an intended bedtime, in favor of continuing evening activities. BTP has been associated with negative sleep outcomes (later timing, shorter duration, poorer quality), and is viewed as a problem of exercising self-control. BTP could be particularly challenging in adolescents, given the combined effects of increasing bedtime autonomy, later chronotype, and a still developing self-control capacity. Thus far, research on BTP has only been based on self-report measures. Here we examined the influence of BTP on adolescent sleep, using objective measures of sleep. MethodsAbout 121 adolescents aged 14-19 years completed a survey on BTP, sleep quality, chronotype, and mental health. Subsequently, habitual sleep was actigraphically monitored for up to 2 weeks, and participants completed a temporal discounting task (a proxy for impulsivity). Associations between BTP, chronotype, and actigraphy-measured sleep were examined for school nights and non-school nights separately. ResultsGreater BTP was associated with poorer subjective sleep, eveningness chronotype, and higher daytime fatigue, as well as higher anxiety/depression scores. Measured using actigraphy, greater BTP predicted later bedtimes and shorter sleep duration on school nights, even when controlling for chronotype. On non-school nights, eveningness chronotype, but not BTP, predicted later bedtimes and wake-up times. BTP was not correlated with temporal discounting. ConclusionsBedtime procrastination exerts significant influence on subjective and objective sleep measures in adolescents. Its effects are most prominent on school nights and can be separated from the effects of chronotype, which has stronger effects on sleep timing on non-school nights.

Bedtime procrastination and chronotype have a differential impact on objectively measured sleep among adolescents on school nights and non-school nights

Bedtime procrastination and chronotype have a differential impact on objectively measured sleep among adolescents on school nights and non-school nights

What time do you plan to sleep tonight? An intense longitudinal study of adolescent daily sleep self-regulation via planning and its associations with sleep opportunity.

Most adolescents are sleep deprived on school days, yet how they self-regulate their sleep-wake behaviours is poorly understood. Using ecological momentary assessment, this intense longitudinal study explored patterns of adolescents' daily bedtime and risetime planning and execution, and whether these behaviours predicted sleep opportunity. Every afternoon, for 2 school weeks and the subsequent 2 vacation weeks, 205 (54.1% female, 64.4% non-White) adolescents from year 10 to 12 (M ± SDage = 16.9 ± 0.9) reported their plans for bedtime (BT) that evening, and for risetimes (RT) the following day. Actual daily sleep was measured via actigraphy and sleep diary. Some adolescents never planned bedtime (school 19.5%, non-school 53.2%) or risetime (school 1.5%, non-school 24.4%). More adolescents planned consistently (≥75% of days) on school (BT = 29.9%, RT = 61.3%) compared to non-school nights (BT = 3.5%, RT = 2.5%). On average, adolescents went to bed later than planned, with longer delays on non-school (71 min) compared to school nights (46 min). Of those who executed their plans within ≤15 min, more did it consistently (≥75% of days) on school (BT = 40.9%, RT = 67.7%) than on non-school nights/days (BT = 29.7%, RT = 58.6%). Mixed effects models utilizing daily data, controlling for sex, race, and study day, showed that bedtime planning predicted longer time in bed (TIB; p < .01) on school and shorter TIB on non-school nights (p < .01); and greater delay in actual (compared to planned) BT predicted shorter TIB (p < .001). Adolescents may require support during the transition from parent-controlled to autonomous sleep self-regulation. Bedtime planning on school nights and going to bed as planned are two modifiable sleep regulatory behaviours that are protective and potential therapeutic targets for increasing sleep opportunity in adolescents.

635 Evening Sunglasses Plus Stable Wake Times: Can This Intervention Help Adolescents with Delayed Sleep-wake Phase Disorder?

Abstract Introduction Evening light can increase alertness and shift the circadian clock later (delay) and, in turn, delay sleep onset timing. We are examining whether reducing evening light exposure (by wearing sunglasses) paired with stable wake times can shift circadian and sleep onset times earlier in adolescents with Delayed Sleep-Wake Phase Disorder (DSWPD). Methods Fifteen adolescents (14.7–18.1 years; 4 male) diagnosed with DSWPD are included in this interim analysis. Participants sleep without restriction at home for a baseline week, and then complete a 2-week intervention. One group (Amber; n=7) wears glasses with amber lenses (Cocoons®, Live Eyewear) beginning 7 hours before their baseline mid-sleep time until self-selected bedtime or for 7 hours (corresponds to the phase delay region of the adolescent phase response curve to light). The amber lens transmits 14% of light to the eye and absorbs most short wavelength light. The frame blocks light from all angles. Amber participants are also instructed to wake at their average baseline school-morning wake-up time (±30 mins). Another group (Clear; n=8) wears identical glasses with clear lenses at the same time and wake time is unrestricted. Glasses wear time is monitored with an iButton placed at the temple tip. Dim Light Melatonin Onset (DLMO) is measured after the baseline week and after the 2-week intervention; 2 participants (one from each group) do not have melatonin data due to technical error. Sleep is measured with wrist actigraphy and sleep diaries throughout the study. DLMO and sleep onset changes (baseline-intervention) are compared between groups with t-tests. Results Amber DLMOs shifted 1.0±2.0h earlier (min-max: 0.4-h delay-5.0-h advance) and Clear DLMOs shifted 0.4±1.1h later (min-max: 2.0-h delay-0.6-h advance) [t(11)=1.60,p=0.14]. Average school-night sleep onset shifted earlier in both groups (Amber:0.4±1.3h; Clear:0.6±1.0h; t(13)=0.2,p=0.8]. Average non-school-night sleep onset shifted 1.1±1.0h earlier in Amber (min-max:0.6-h delay-2.2-h advance) and remained stable (0.03±1.0h) in Clear (min-max: 1.8-h delay-1.7-h advance) [t(13)=1.92,p=.08]. Conclusion Trends from this ongoing study suggest that amber-lensed glasses to block evening light plus stable wake-up times may shift circadian rhythms earlier. This strategy appears to help adolescents with DSWPD fall asleep earlier predominantly on non-school nights. Support (if any) AASMF Strategic Research Award (184-SR-17) to RRA

149 Perceived sleep need, cumulative sleep deficit, and associations with daily affect in adolescents: 28 days over school &amp; vacation

Abstract Introduction This study aimed to describe trajectories of daily perceived sleep need and sleep deficit across 28 consecutive days, and examine if cumulative sleep deficit predicts next-day affect. Methods Daily sleep and affect were measured over 2 weeks of school and 2 weeks of vacation in 205 adolescents (54.1% females, Mage = 16.9 years). Each day, participants completed actigraphy and self-reported the amount of sleep needed to function well the next day (i.e., perceived sleep need), sleep duration, and high- and low-arousal positive and negative affect. Cumulative actigraphy and diary sleep deficit were calculated as difference between perceived sleep need and sleep duration, weighted by sleep deficit over the past 3 days. Cross-lagged, multilevel models were used to test cumulative sleep deficit as a predictor of next-day affect. Lagged affect, day of the week, study day, and sociodemographics were controlled. Results Perceived sleep need was lower early in the school week, before increasing in the second half of the week. Adolescents accumulated sleep deficit across school days and reduced it during weekends. During weekends and vacations, adolescents’ self-reported, but not actigraphy sleep duration, met perceived sleep need. Higher cumulative actigraphy sleep deficit predicted higher next-day high arousal negative affect; higher cumulative diary sleep deficit predicted higher negative affect (regardless of arousal), and lower low arousal positive affect the following day. Conclusion Adolescents experienced sustained cumulative sleep deficit across school days, and whilst non-school nights appeared to be opportunities for reducing sleep deficit. Trajectories of sleep deficit during vacation suggested recovery from school-related sleep restriction. Cumulative sleep deficit was related to affect on a daily basis, highlighting the value of this measure for future research and interventions. Support (if any):

Genetic and environmental influences on sleep-wake behaviors in adolescence.

To investigate the influence of genetic and environmental factors on sleep-wake behaviors across adolescence. Four hundred and ninety-five participants (aged 9-17; 55% females), including 93 monozygotic and 117 dizygotic twin pairs, and 75 unmatched twins, wore an accelerometry device and completed a sleep diary for 2 weeks. Individual differences in sleep onset, wake time, and sleep midpoint were influenced by both additive genetic (44%-50% of total variance) and shared environmental (31%-42%) factors, with a predominant genetic influence for sleep duration (62%) and restorative sleep (43%). When stratified into younger (aged 9-14) and older (aged 16-17) subsamples, genetic sources were more prominent in older adolescents. The moderate correlation between sleep duration and midpoint (rP = -.43, rG = .54) was attributable to a common genetic source. Sleep-wake behaviors on school and nonschool nights were correlated (rP = .44-.72) and influenced by the same genetic and unique environmental factors. Genetic sources specific to night-type were also identified, for all behaviors except restorative sleep. There were strong genetic influences on sleep-wake phenotypes, particularly on sleep timing, in adolescence. Moreover, there may be common genetic influences underlying both sleep and circadian rhythms. The differences in sleep-wake behaviors on school and nonschool nights could be attributable to genetic factors involved in reactivity to environmental context.

Pupils with negative social jetlag in Japan are hypothesised to constitute a discrete population

Social jetlag (SJL) is the difference between the midpoint time of sleep between weekend and weekday. SJL is experienced by many adolescents and is a matter of concern in public health. In private experiences, pupils who were forced to wake up early on non-school day mornings to engage in hard physical activity and who had little time for sleep on non-school nights were identified. These pupils are supposed to show negative SJL values. However, few studies have described pupils with negative SJL values. The current study aimed to raise a hypothesis that pupils with negative SJL might constitute a discrete population with typical characteristic features. A total of 2,722 questionnaires obtained from pupils in grades 5 to 12 were assessed by the multiple comparison test of the Dunnett method to determine significant differences in the lifestyle habits among SJL categories (SJL-I, <0 h; SJL-II, 0–1 h; SJL-III, 1–2 h; SJL-IV, 2 h or more). Pupils with SJL-I were mostly male compared to those in SJL-II. Physical activity score showed a significantly higher value in SJL-I than SJL-II, while that in SJL-III and SJL-IV revealed significantly lower values than that in SJL-II. Contrarily, non-school day screen time and non-school night sleep duration showed significantly lower values in SJL-I than SJL-II, while those in SJL-III and SJL-IV revealed significantly higher values than those in SJL-II. After-school activity score showed a significantly higher value in SJL-I than SJL-II, while that in SJL-III and SJL-IV revealed lower values than that in SJL-II. At least in Japan, a hypothesis that pupils with negative SJL constitutes a discrete population with typical characteristic features (predominantly male, higher physical activity, and after-school activity scores; less non-school day screen time; and shorter non-school night sleep duration) was raised. Further studies are needed to confirm this hypothesis.

Self-reported, Sleep Behaviors And Barriers Of Adolescent Athletes

BACKGROUND: Many adolescent athletes fail to meet sleep duration recommendations, however little is known about self-reported barriers preventing adequate sleep. PURPOSE: To investigate current and ideal sleep behaviors, as well as barriers to sleep in a group of adolescent athletes. METHODS: Adolescent athletes (n = 258, 16.3 ± 1.4 y; 196 male, 62 female) from 8 sports completed a standardized electronic survey regarding sleep behaviors and perceived barriers to sleep. The survey assessed current and ideal sleep onset, offset, and duration. Athletes were presented a list of common pre-bed activities and asked if they ever engaged in each activity in the hour prior to bed. Similarly, a list of potential barriers to falling asleep once in bed were presented. Frequency of all barriers (nights per week) was assessed. Comparisons between ideal and current behaviors were made using ANOVA and t-tests as appropriate. Barriers were ranked to identify which were most responsible for impacting sleep behaviors. Normally and non-normally distributed data are presented as mean ± SD, and mean with associated 95% confidence interval [95% CI], respectively. RESULTS: Significant differences were found between ideal (9:24 ± 1:18 (h:min)) and school night (8:14 ± 1:43), ideal and non-school night (9:56 ± 1:57), and school and non-school night sleep durations (p<0.05). The most frequent pre-bed activities were: engaging in social media (5.6 nights per week, 95% CI [5.3,5.9]), communication with others using technology (4.9 [4.6,5.2]), and socializing (4.9 [4.6,5.2]). School work (3.0 [2.7,3.2]) was the most cited barrier to getting into bed at the desired time, followed by engaging with social media (2.6 [2.2,2.9]) and communicating using technology (2.1 [1.8,2.4]). Once in bed, worrying about school work (1.5 [1.2,1.7]) and worrying about sport (1.4 [1.1,1.7]) were the two most cited barriers to falling asleep. CONCLUSION: Discrepancies exist between actual and desired sleep duration with athletes preferring to obtain more sleep on school nights. School work as well as the use of technology are top barriers preventing adolescent athletes from achieving their ideal bedtime. Focus should be placed on behavior change strategies, including time management, to overcome these known barriers to sleep.

Changes in Sleep Duration and Timing During the Middle-to-High School Transition

PurposeThe purpose of the study was to quantify changes in sleep during the middle-to-high school transition and determine if changes in sleep differ by sociodemographic characteristics. MethodsAdolescents were enrolled in eighth grade and followed into ninth grade (N = 110; 2,470 nights observed). The outcomes were actigraphy-estimated sleep duration, sleep onset, sleep offset, and sleep sufficiency (≥8 hours of sleep). The exposures were school grade (eighth or ninth), school night status (school or nonschool), sex (female or male), and race (white, black, or other). ResultsOn school nights, sleep duration declined by 25.8 minutes per night (p < .001) from eighth to ninth grade. There was no change in sleep duration on nonschool nights. Timing of sleep onset was 22.2 minutes later on school nights (p < .001) and 17.4 minutes later on nonschool nights (p < .001) in ninth grade. Timing of sleep offset did not change on school mornings but was 22.2 minutes later on nonschool mornings (p < .001) in ninth grade. The proportion of school nights (and nonschool nights) with sleep duration ≥8 hours was 9.4% (38.3%) in eighth grade and 5.7% (35.9%) in ninth grade. The odds of sleeping ≥8 hours per night was 42% lower in ninth grade, compared toeighth grade (odds ratio = .58; 95% confidence interval: .37, .91). Males were 59% less likely to sleep ≥8 hours per night. Black adolescents were 51% less likely to sleep ≥8 hours per night. ConclusionsInsufficient sleep is highly prevalent, especially on school nights and among male and black adolescents, and this problem worsens with the transition to high school.

Sleep in New Zealand children aged 7-9: associations with ethnicity, socioeconomic status, and achievement in reading and mathematics.

The aims were (1) to investigate differences by ethnicity and socioeconomic status (SES) in objective measures of sleep in children aged 7-9 years and (2) determine whether measures of sleep predict child achievement in reading or mathematics after controlling for ethnicity and SES. Four groups of parent-child dyads were recruited: Māori, low-SES schools (n = 18); Māori, high-SES schools (n = 17); New Zealand European, low-SES schools (n = 18); New Zealand European, high-SES schools (n = 17). Child sleep was measured by actigraphy. Parents and teachers reported child daytime sleepiness and behavior, and children completed a self-report of anxiety symptoms. Teachers also reported on child achievement in reading and mathematics. Children from low-SES schools went to bed later on school nights (F[1,68] = 12.150, P = .001) and woke later (F[1,68] = 15.978, P < .001) than children from high-SES schools but had similar sleep duration. There were no differences related to ethnicity. Children from low-SES schools were almost 3 times more likely to be below national standards for mathematics. Children not meeting academic standards in mathematics had a later sleep start time, lower sleep period efficiency, and a decreased total sleep time. However, when SES and sleep period efficiency were modeled together neither were found to significantly influence achievement in mathematics. In this study, SES influenced sleep timing but not the quality and quantity of sleep in 7- to 9-year-old children, and a significant independent effect of sleep efficiency on learning could not be demonstrated.

0963 Perceived Sleep Need in Adolescents and Acceptability of a Morning Bright Light Intervention

Abstract Introduction We are testing strategies to make fall asleep times earlier to extend sleep duration of sleep-deprived, delayed adolescents. We explored whether perceived sleep need was associated with the acceptability of an intervention which included earlier bedtimes on school nights and early morning bright light (BL) on one weekend. Methods Twenty-three participants (16.0±0.7 years; N=11 female) from the intervention group of a study of adolescents reporting ≤7 h sleep on school nights and late bedtimes (school-night≥23:00; non-school night≥midnight) were included. Participants estimated nightly sleep need during baseline. The intervention included shifting school-night bedtimes 1h earlier than each participant’s usual school night bedtime during the first week and 2h earlier than baseline during the second week. Evening time management goals were used to help participants get to bed earlier. Wake times remained stable because of early school start times. During the weekend in between these 2 weeks, participants lived in the laboratory and received very early morning BL on Saturday and Sunday from two light boxes (~6000 lux; three 50-minute exposures with 10-minute breaks). At the end of the study, an acceptability questionnaire asked about hypothetically engaging in different elements of the intervention (earlier bedtimes, morning BL) at home. Items were summed to create a composite acceptability score. Results Participants reported needing 7.8 h (SD=1.2; range=5-9.5h) of sleep. A linear regression demonstrated that needing more sleep was associated with less acceptability when controlling for age and sex, b=1.37, SEb=.60, p=.03. Conclusion Adolescents who felt they needed less sleep were more accepting of an intervention that required earlier bedtimes on school nights and waking up very early to receive BL on the weekend. Interventions that produce less weekend sleep deprivation, like including naps, might be more acceptable to all. Support R01HL105395 (S.J.C.)

0339 Where Does the Time Go? Reported Activities Around the DLMO in Older Adolescents

Abstract Introduction Older adolescents show heightened alertness in the evening close to the time of their Dim Light Melatonin Onset (DLMO), a time when the circadian system is most responsive to delaying light. We examined reported activities of adolescents around the time of their DLMO. Methods Forty-six adolescents (14.2-17.9 years; 24 females) who reported ≤7 h sleep on school nights and late bedtimes (school-night ≥ 23:00; non-school night ≥ midnight) slept at home on their usual school-year sleep schedule for 2 weeks. Participants reported their main activity via text message every hour from 16:00 until self-selected bedtime. After these 2 weeks, their DLMO was measured. We examined reported activities in the hour around the DLMO and the 2 hourly responses that followed on weeknights (Sunday-Thursday) to determine the most common activities (n=1380 responses). Logistic regression tested whether frequency of activities predicted whether a participant’s DLMO fell within the earliest (n=15; 19:31 ± 00:44), middle (n=16; 20:49 ± 00:20), or latest (n=15; 22:29±1:15) tertile. Results Overall, reported activities that consumed the most time were cell phone use (19.5%), homework (18.3%), and watching TV (15.1%). Adolescents who reported more homework, were more likely to have a DLMO in the middle tertile compared to the earliest and latest tertiles. Cell phone use was least likely in adolescents in the earliest DLMO tertile. TV watching did not predict DLMO group. Conclusion Adolescents who had the earliest DLMOs spent less time on their phones when light has the greatest delaying effect. These data may indicate that light from cell phone screens may delay circadian phase in this age group. Alternatively, cell phone use may be more likely if adolescents cannot fall asleep due to a later circadian cue for sleep onset. Support R01 HL112756 (Crowley)

0390 Associations of the Neighborhood Built Environment with Adolescent Sleep Outcomes

Abstract Introduction Over 75% of U.S. high school students obtain insufficient amounts of sleep. Identification of modifiable environmental determinants of adolescent sleep is needed to inform interventions and public health strategies, yet little is known about the influence of the built environment on adolescent sleep. We examined associations of the built environment with objectively-measured adolescent sleep outcomes. Methods In this longitudinal, prospective study, we used actigraphy to assess sleep outcomes for 14 days each in 8th grade and 9th grade: duration (hours/night), onset (hours from 00:00), offset (hours from 00:00), and sleeping &amp;gt;8 hours. Home addresses were linked to built environment exposures based on half-mile Euclidian buffers (overall/human-made sound levels, percent tree canopy cover, street density, intersection density) and census block group (population density, housing density). Mixed-effects linear (sleep duration, onset, offset) and logistic (&amp;gt;8 hours) regression estimated associations of each built environment measure with sleep outcomes, adjusting for sex, race, parent education, household income, grade and weeknight status (school or non-school night). Results Among 108 adolescents - 53% female and 25% Black - providing 2,388 nights of sleep data across 8th and 9th grades, a 1-standard deviation increase in neighborhood sound (overall and human-made) associated with 11 minutes later sleep onset (β=0.19; 95% CI: 0.01, 0.38) and 20% lower odds of sleeping for &amp;gt;8 hours (OR=0.80, 95% CI: 0.62, 1.02). A 1-standard deviation increase in neighborhood tree canopy cover associated with 11 minutes earlier sleep onset (β= -0.19, 95% CI: -0.35, -0.03) and 7 minutes earlier sleep offset (β= -0.12, 95% CI: -0.23, -0.02). No associations were observed for “density based” exposures. Conclusion Higher tree canopy cover associated with more favorable sleep timing while higher neighborhood sound level associated with later timing of sleep onset. These modifiable neighborhood built environment factors should be considered when intervening to support healthier sleep among adolescents. Support NIH/NHLBI K01HL123612 (JM) and Sleep Research Society Foundation and K23HD094905 (AAW)

Longitudinal Change in Adolescent Bedtimes Measured by Self-Report and Actigraphy.

Introduction:Sleep is often quantified using self-report or actigraphy. Self-report is practical and less technically challenging, but prone to bias. We sought to determine whether these methods have comparable sensitivity to measure longitudinal changes in adolescent bedtimes.Methods:We measured one week of free-living sleep with wrist actigraphy and usual bedtime on school nights and non-school nights with self-report questionnaire in 144 students at 15 y and 17 y.Results:Self-reported and actigraphy-measured bedtimes were correlated with one another at 15 y and 17 y (p < .001), but reported bedtime was consistently earlier (>30 minutes, p < .001) and with wide inter-method confidence intervals (> ±106 minutes). Mean inter-method discrepancy did not differ on school nights at 15 y and 17 y but was greater at 17 y on non-school nights (p = .002). Inter-method discrepancy at 15 y was not correlated to that at 17 y. Mean change in self-reported school night bedtime from 15 y to 17 y did not differ from that by actigraphy, but self-reported bedtime changed less on non-school nights (p = .002). Two-year changes in self-reported bedtime did not correlate with changes measured by actigraphy.Conclusions:Although methods were correlated, consistently earlier self-reported bedtime suggests report-bias. More varied non-school night bedtimes challenge the accuracy of self-report and actigraphy, reducing sensitivity to change. On school nights, the methods did not differ in group-level sensitivity to changes in bedtime. However, lack of correlation between bedtime changes by each method suggests sensitivity to individual-level change was different. Methodological differences in sensitivity to individual- and group-level change should be considered in longitudinal studies of adolescent sleep patterns.

Impact of Sleep Duration and Weekend Oversleep on Body Weight and Blood Pressure in Adolescents.

Weekend oversleep or catchup sleep is a frequent occurrence in children, but there are relatively little data concerning its impact on weight and blood pressure. The aim of this study was to assess the association between sleep duration and oversleep, and weight and blood pressure in adolescents. Sleep duration, weight and blood pressure of 327 children (51.4% boys, mean age 13.3 ± 1.7 years) who had polysomnograms performed during both exam cycles of the Tucson Children's Assessment of Sleep Apnea study (TuCASA) were analyzed. Sleep duration on school nights and non-school nights was used to compute a weighted average of child and parent reported overall sleep duration respectively. Oversleep was defined as the difference between self and parent reported weekend sleep and weekday sleep separately. Simple correlations between overall sleep duration, sleep on school and non-school nights and oversleep, and blood pressure, standardized body mass index (BMI), snoring, respiratory disturbance index (RDI) and age were calculated. Significant bivariate associations then were used to develop multivariate partial correlation models. Unadjusted negative correlations with BMI were noted for parent reported total sleep duration at the 1st exam cycle, parent and child reported total sleep and school night sleep duration, and parent reported non-school night sleep duration at the 2nd exam cycle. Additionally, for BMI, positive correlations were observed for log RDI at both exam cycles and snoring at the 2nd exam cycle. For blood pressure, there were positive associations with age, parent reported oversleep, caffeine consumption and snoring. Additionally, for blood pressure, negative relationships were observed with parent reported total sleep duration at the 1st exam cycle, and parent and child reported total sleep and school night sleep durations at the 2nd exam cycle. Partial correlations found that BMI was negatively correlated with parent reported total sleep duration at the 1st exam cycle and parent reported total sleep duration at the 2nd exam cycle, and positively correlated with snoring and log RDI at both exam cycles. Systolic blood pressure was only associated with age and snoring. Diastolic blood pressure was positively correlated with age and caffeine consumption, and negatively correlated with parent reported total and school night sleep duration. Oversleep and child reported sleep duration were not represented in any of these models. Lower amounts of sleep especially on school nights is associated with higher body weight and blood pressure. Oversleep was not associated with either body weight or blood pressure.

Can working memory training improve children's sleep?

BackgroundImproving children's sleep could lead to significant benefits in several functional domains. Recent research in adults suggests that intensive, adaptive cognitive training may be beneficial in improving sleep, although there is limited understanding whether this approach yields similar results in children. ObjectiveTo determine whether a working memory training program improved sleep latency, sleep problems, and sleep duration on school and nonschool nights and whether there was a differential effect of the timing of training during the school day on sleep outcomes. Design/MethodsDesign: Population-based randomised controlled trial. Setting: Forty-four schools in Melbourne, Australia. Participants: All Grade 1 children (mean age = 6.9 years, SD 0.4) underwent WM screening using two subtests from the Automated Working Memory Assessment. Children with low verbal and/or visuo-spatial WM scores relative to their peers (‘low WM’, ∼25%) were randomised to intervention or control arms. Intervention: 20 to 25 computerised 25-min training sessions were conducted using the CogMed program, over 5–7 weeks at school. Outcomes: Parent-reported child sleep characteristics (time, latency, duration and problem) at 6 months post randomisation. ResultsA total of 452 (26.0%) of 1723 children screened (64.1% of approached) met trial eligibility criteria, with 226 in each study arm. Of intervention children, 91% completed the minimum 20 days of training. Retention was 90.5% at 6 months. Adjusted regressions showed that intervention children did not have better sleep latency, duration, bedtime consistency or less sleep problems. ConclusionIt does not appear that adaptive working memory training during the school day can be used as a novel approach to improve children's sleep attributes up to 6 months post-randomisation, regardless of the time of day training is delivered.