Sleep deficiency on school days in Icelandic youth, as assessed by wrist accelerometry

Social jetlag and obesity: A systematic review and meta-analysis

Social jetlag, the weekly variation in sleep timing, is proposed to contribute to increased obesity risk, potentially because of the misalignment of behavioral cycles relative to the endogenous circadian timing system. This systematic review and meta-analysis aim to determine the association between social jetlag and adiposity-related measures using observational studies. We reviewed 477 references, of which 43 studies met inclusion criteria with a total sample size of 231,648. There was a positive association between social jetlag and body mass index (correlation coefficient [r]: 0.12; 95%CI, 0.07, 0.17; P < 0.001; I2  = 94.99%), fat mass (r: 0.10; 95%CI, 0.05, 0.15; P < 0.001; I2  = 0.00%), fat mass index (fat mass divided by height in meter squared, β: 0.14 kg/m2 ; 95%CI, 0.05, 0.23; P < 0.001; I2  = 56.50%), percent of body fat (r: 0.37; 95%CI, 0.33, 0.41; P < 0.001; I2  = 96.17%), waist circumference (r: 0.15; 95%CI, 0.06, 0.24; P = 0.001; I2  = 90.83%), and the risk of having overweight/obesity (odds ratio: 1.20; 95%CI, 1.02, 1.140; P = 0.039; I2  = 98.25%). Social jetlag is positively and consistently associated with multiple obesity-related anthropometric measures. Further studies are needed to test causality, underlying mechanisms, and whether obesity interventions based on increasing regularity of the sleep/wake cycle can aid in the battle against the obesity pandemic.

Quantifying teenagers' sleep patterns and sex differences in social jetlag using at-home sleep monitoring

Sleep plays an important role in the formative developmental processes occurring during the teenage years. At the same time, teenagers' changing bioregulatory mechanisms and psychosocial factors converge into the so-called social jetlag, a sleep timing misalignment between weekdays and weekends. The aim of this study was to quantify the course of day-to-day changes in sleep/wake patterns and sleep stage distributions, and the sex differences in social jetlag among teenagers. We observed the sleep of 156 teenagers (58.3% girls, 15-16 years) using a novel sleep monitor over the course of up to 10 consecutive days. 1323 nights of data were analyzed using multilevel modeling. On average, participants went to bed at 23:41, woke up at 07:48, slept for 7.7 h and had 85.5% sleep efficiency. Sleep stage distributions were in line with normative data. We found later sleep onset and offset, longer time in bed, sleep duration, and sleep onset latency (p = .001), greater proportion of light sleep and lower proportion of deep sleep, and poorer sleep efficiency (all p < .001) on weekend nights starting on Friday and Saturday. On Friday nights, girls had longer time awake after sleep onset (p = .020) than boys. On Friday and Saturday nights, girls fell asleep earlier (p < .001 and p = .006, respectively). On Saturday nights, girls had shorter sleep latency (p = .024), and better sleep efficiency (p = .019) than boys. In sum, teenagers' sleep patterns reflected healthy, albeit somewhat short sleep. There was convincing evidence of social jetlag, and girls exhibited less severe social jetlag than boys.

Characteristic Sleep Patterns and Associated Obesity in Adolescents

Poor sleep adversely affects health and may cause obesity. Poor sleep includes short sleep duration, low quality of sleep, and sleep discrepancy. Although most studies have focused on the association between sleep duration and obesity, poor sleep is a significant risk factor for obesity. Adolescents have characteristic sleep patterns which correspond to poor sleep. Adolescents sleep late due to various biological and psychosocial factors; also, they wake up early to be on time for school. This causes them to sleep less. To make up for this sleep debt, adolescents sleep more on non-school days, which causes sleep discrepancies. Therefore, since adolescents have characteristic sleep patterns, an in-depth investigation is needed to identify whether poor sleep is a risk for obesity. This article presents an overview of the characteristic sleep patterns of adolescents, and reviews studies on the association of each sleep pattern with obesity.

Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study

Background

The gold standard measurement for recording sleep is polysomnography performed in a hospital environment for 1 night. This requires individuals to sleep with a device and several sensors attached to their face, scalp, and body, which is both cumbersome and expensive. Self-trackers, such as wearable sensors (eg, smartwatch) and nearable sensors (eg, sleep mattress), can measure a broad range of physiological parameters related to free-living sleep conditions; however, the optimal duration of such a self-tracker measurement is not known. For such free-living sleep studies with actigraphy, 3 to 14 days of data collection are typically used.

Objective

The primary goal of this study is to investigate if 3 to 14 days of sleep data collection is sufficient while using self-trackers. The secondary goal is to investigate whether there is a relationship among sleep quality, physical activity, and heart rate. Specifically, we study whether individuals who exhibit similar activity can be clustered together and to what extent the sleep patterns of individuals in relation to seasonality vary.

Methods

Data on sleep, physical activity, and heart rate were collected over 6 months from 54 individuals aged 52 to 86 years. The Withings Aura sleep mattress (nearable; Withings Inc) and Withings Steel HR smartwatch (wearable; Withings Inc) were used. At the individual level, we investigated the consistency of various physical activities and sleep metrics over different time spans to illustrate how sensor data from self-trackers can be used to illuminate trends. We used exploratory data analysis and unsupervised machine learning at both the cohort and individual levels.

Results

Significant variability in standard metrics of sleep quality was found between different periods throughout the study. We showed specifically that to obtain more robust individual assessments of sleep and physical activity patterns through self-trackers, an evaluation period of >3 to 14 days is necessary. In addition, we found seasonal patterns in sleep data related to the changing of the clock for daylight saving time.

Conclusions

We demonstrate that >2 months’ worth of self-tracking data are needed to provide a representative summary of daily activity and sleep patterns. By doing so, we challenge the current standard of 3 to 14 days for sleep quality assessment and call for the rethinking of standards when collecting data for research purposes. Seasonal patterns and daylight saving time clock change are also important aspects that need to be taken into consideration when choosing a period for collecting data and designing studies on sleep. Furthermore, we suggest using self-trackers (wearable and nearable ones) to support longer-term evaluations of sleep and physical activity for research purposes and, possibly, clinical purposes in the future.

Sex differences in childhood sleep and health implications

CONTEXT

Sleep is critical for optimal childhood metabolic health and neurodevelopment. However, there is limited knowledge regarding childhood sex differences in sleep, including children with neurodevelopmental disorders, and the impact of such differences on metabolic health.

OBJECTIVE

To evaluate if sex differences in childhood sleep exist and if sleep associates with metabolic health outcomes equally by sex. Using autism spectrum disorder (ASD) as a case study, we also examine sleep sex differences in children with a neurodevelopmental disorder.

METHODS

A narrative review explored the literature focussing on sex differences in childhood sleep.

RESULTS

Sex differences in sleep were not detected among pre-adolescents. However, female adolescents were more likely to report impaired sleep than males. Childhood obesity is more common in males. Shorter sleep duration may be associated with obesity in male pre-adolescents/adolescents; although findings are mixed. ASD is male-predominant; yet, there was an indication that pre-adolescent female children with ASD had more impaired sleep.

CONCLUSION

Sex differences in sleep appear to emerge in adolescence with more impaired sleep in females. This trend was also observed among pre-adolescent female children with ASD. Further research is needed on sex differences in childhood sleep and metabolic health and the underlying mechanisms driving these differences.

The Future of Sleep Measurements: A Review and Perspective

This article provides an overview of the current use, limitations, and future directions of the variety of subjective and objective sleep assessments available. This article argues for various ways and sources of collecting, combining, and using data to enlighten clinical practice and the sleep research of the future. It highlights the prospects of digital management platforms to store and present the data, and the importance of codesign when developing such platforms and other new instruments. It also discusses the abundance of opportunities that data science and machine learning open for the analysis of data.

Sleep timing and consistency are associated with the standardised test performance of Icelandic adolescents

Sleep has been shown to affect cognitive function in laboratory studies; however, its association to the academic performance of adolescents has largely been demonstrated using self-reported measures. Studies with objective measures of both sleep and academic performance are limited. The aim of the present study was to determine whether the free-living sleep quantity, quality, and timing of 15-year-old adolescents measured with wrist actiography are associated with their scores on national standardised examinations as an objective measure of academic achievement. We measured sleep with wrist actiography for 1 week in 253 (150 girls) Icelandic adolescents with a mean (SD) age of 15.9 (0.3) years. Multiple linear regression was used to assess associations between sleep parameters and combined standardised examination scores in mathematics, English, and Icelandic obtained from the Icelandic Directorate of Education. We found that students went to bed at 00:49 hours (± 51.8 min) and slept for a mean (SD) of 6.6 (0.7) hr/night, with a median (interquartile range) night-to-night variation in sleep duration of 1.2 (0.7) hr and an efficiency of 88.1 (5.3)%. Combined analyses adjusted for sex, demonstrated that both bedtime and night-to-night variability in total sleep time were negatively associated with the average score across all topics. Sex-specific associations did not indicate clear differences between boys and girls. These findings suggest that, in addition to appropriate sleep duration, public health guidance should also highlight the importance of early and consistent sleep schedules to academic achievement for both boys and girls.

Cross-sectional associations between objectively measured sleep characteristics and body mass index in European children and adolescents

BACKGROUND

Short sleep duration has been found to be associated with a higher risk for overweight and obesity. However, previous studies have mainly relied on subjective measures of sleep duration and other sleep characteristics (eg quality, timing) have often been neglected. Therefore, we aimed to investigate associations between several, mainly objectively measured sleep characteristics and body mass index (BMI). Further, we aimed to identify distinct sleep subtypes based on these sleep characteristics and to study their association with BMI.

METHODS

Children aged 9-16 years participating in the European I.Family study (N = 559, 51.2% girls, 32.9% overweight/obese) wore an accelerometer for one week on their wrist and recorded their daily wake-up and lights-off times in a sleep diary. Information on sleep duration, sleep efficiency and sleep latency was derived. To identify sleep subtypes, we conducted a latent class analysis using all five sleep variables. Associations between single sleep variables, sleep subtype and age- and sex-specific BMI z-score were investigated using linear mixed-effects regression models to accommodate clustering among siblings.

RESULTS

No statistically significant associations were observed between the single sleep variables (sleep duration, sleep efficiency, sleep latency, wake-up and lights-off times) and BMI z-score. Four sleep subtypes were identified and children were assigned to one of the groups based on their highest probability for latent group membership: "early birds" (17.5% of the sample), "short sleep duration" (14.7%), "optimal sleep" (47.6%) and "poor sleep quality" (20.2%). Sleep subtype was not associated with BMI z-score.

CONCLUSIONS

Using objective sleep data, we did not find convincing evidence for associations between the sleep variables under investigation and BMI.

A meta-analysis of accelerometer sleep outcomes in healthy children based on the Sadeh algorithm: the influence of child and device characteristics

STUDY OBJECTIVES

Children often experience sleep problems, with a negative impact on mood, behavior, cognitive function, and other aspects of mental and physical health. Accelerometers are widely used to assess sleep, but general reference values for healthy children do not yet exist. The aim of this meta-analysis was to determine mean values for wake after sleep onset (WASO), sleep efficiency (SE), total sleep time (TST) and sleep onset latency (SOL), and to determine the effect of child and accelerometer-characteristics.

METHODS

A search included studies with healthy children, 0-18 years, reporting WASO, SE, TST, and/or SOL, calculated with the Sadeh algorithm. Meta-analyses with random effects produced pooled estimate means per outcome. Meta-regression analyses determined the effect of age, sex, placement site and accelerometer type.

RESULTS

Eighty-three studies (9,068 participants) were included. Pooled means were 63 min (95% CI 57 to 69) for WASO, 88% (95% CI 87 to 89) for SE, 477 min (95% CI 464 to 491) for TST and 19 min (95% CI 17 to 22) for SOL. Heterogeneity was high (95%-99%). TST decreased with age and there was an age-effect on SOL. SE differed between wrist and ankle (used in age 0-24 months) placement, and between piezoelectric and MEMS-type accelerometers. No differences were found between boys and girls, although this number of studies was small.

CONCLUSIONS

We found differences in almost all investigated outcomes and heterogeneity was high. Therefore, we advise to use a study-specific control sample until more robust reference values are available. Future research should narrow the methodological heterogeneity and produce larger datasets, needed to establish these reference values.

Organized Sport Participation, Physical Activity, Sleep and Screen Time in 16-Year-Old Adolescents

This study aimed to examine the association of different frequencies of organized sport participation (OSP) with physical activity, cardiorespiratory fitness, body composition, sleep, and screen time among adolescents. A cross-sectional study involving 315 16-year-old adolescents was conducted. OSP was self-reported, being categorized as 0 times a week, less than three times a week, 4-5 times a week, and 6-7 times a week, on average. Screen time was also self-reported but physical activity and sleep duration were objectively measured. Cardiorespiratory fitness and body composition were measured using a maximal cycle ergometer test and a dual-energy X-ray absorptiometry (DXA) scan, respectively. An analysis of covariance revealed a significant association between OSP and physical activity (F (3, 286) = 14.53, p < 0.01), cardiorespiratory fitness (F (3, 236) = 17.64, p < 0.01), screen time (F (3, 294) = 8.14, p < 0.01), body fat percentage (F (3, 292) = 11.84, p < 0.01), and fat free mass (F (3, 290) = 5.76, p < 0.01. No significant association was found between OSP and sleep duration. Post hoc analyses showed that OSP at least four times a week was beneficial to favorable physical activity levels, cardiorespiratory fitness, screen time, and body composition and may therefore serve as a valuable tool in battling unhealthy lifestyle behaviors among adolescents.

Sleep Quality and Duration in European Adolescents (The AdolesHealth Study): A Cross-Sectional, Quantitative Study

Sleep is a vital element of adolescents' overall health; it influences their body and mind and thus affects their quality of life. Adequate sleep quality and duration are essential for maintaining optimal metabolic health and lowering the risk of developing several medical conditions, such as cardiovascular disease. The current study aimed to assess the perceived sleep quality and duration of 1717 European adolescents from three different European countries (Spain, Iceland and Estonia) aged 13- to 16-years (900 boys, 817 girls) using the Pittsburgh Sleep Quality Index (PSQI). A multivariate analysis of variance (MANOVA) was performed to examine differences between groups and two-factor analysis of variance (ANOVA) was used to analyze city and age differences. The probability of having poor sleep quality and duration was calculated by Odd-Ratio (OR). Our study found poor sleep quality in 44% of the boys and 53% of the girls, whereas 68% and 69%, respectively did not get the recommended hours of sleep (i.e., 8-10 h). No difference was found between adolescents from Estonia, Iceland and Spain regarding sleep duration. In contrast, Spanish and Estonian adolescents reported higher probabilities of having poor sleep quality. Finally, girls had a significantly higher probability of poor sleep quality than boys.

Association between free-living sleep and memory and attention in healthy adolescents

In laboratory studies, imposed sleep restriction consistently reduces cognitive performance. However, the association between objectively measured, free-living sleep and cognitive function has not been studied in older adolescents. To address this gap, we measured one week of sleep with a wrist-worn GT3X+ actigraph in 160 adolescents (96 girls, 17.7 ± 0.3 years) followed by assessment of working memory with an n-back task and visual attention with a Posner cue-target task. Over the week, participants spent 7.1 ± 0.8 h/night in bed and slept 6.2 ± 0.8 h/night with 88.5 ± 4.8% efficiency and considerable intra-participant night-to-night variation, with a standard deviation in sleep duration of 1.2 ± 0.7 h. Sleep measures the night before cognitive testing were similar to weekly averages. Time in bed the night before cognitive testing was negatively associated with response times during the most challenging memory task (3-back; p = 0.005). However, sleep measures the night before did not correlate with performance on the attention task and weekly sleep parameters were not associated with either cognitive task. Our data suggests shorter acute free-living sleep may negatively impact difficult memory tasks, however the relationship between free-living sleep and cognitive task performance in healthy adolescents is less clear than that of laboratory findings, perhaps due to high night-to-night sleep variation.

Sleep deprivation and its association with diseases- a review

Sleep deprivation, a consequence of multiple health problems or a cause of many major health risks, is a significant public health concern in this era. In the recent years, numerous reports have been added to the literature to provide explanation and to answer previously unanswered questions on this important topic but comprehensive updates and reviews in this aspect remain scarce. The present study identified 135 papers that investigated the association between sleep deprivation and health risks, including cardiovascular, respiratory, neurological, gastrointestinal, immunology, dermatology, endocrine, and reproductive health. In this review, we aimed to provide insight into the association between sleep deprivation and the development of diseases. We reviewed the latest updates available in the literature and particular attention was paid to reports that detailed all possible causal relationships involving both extrinsic and intrinsic factors that may be relevant to this topic. Various mechanisms by which sleep deprivation may affect health were presented and discussed, and this review hopes to serve as a platform for ideas generation for future research.

Changes in sleep and activity from age 15 to 17 in students with traditional and college-style school schedules

OBJECTIVES

Sleep duration and physical activity decline with age during adolescence. Earlier school schedules may contribute to these declines. The aim of this longitudinal study was to track changes in sleep and activity of Icelandic youth from primary to secondary school and compare students who enrolled in secondary schools with traditional and college-style schedules.

METHODS

We measured free-living sleep and activity with wrist actigraphy and body composition by dual-energy x-ray absorptiometry in 145 students at age 15 and age 17, when 58% attended schools with college-style scheduling. Differences from 15 to 17 and between students of different school structures were assessed with mixed-effect models.

RESULTS

Actigraphs were worn for 7.1 ± 0.4 nights at 15 and 6.9 ± 0.4 nights at 17. Overall, sleep duration decreased from 6.6 ± 0.7 h/night to 6.2 ± 0.7 h/night from age 15 to 17 (P < .001). Students with traditional schedules reduced school-night sleep duration 26 min/night at follow-up (P< .001), while sleep duration did not change for college-style students. All students went to bed later on school nights at follow-up, but only college-style students rose later. Sleep efficiency and awakenings did not differ by schedule-type. Neither sex changed body fat percentage, but average school-day activity decreased by 19% (P< .001) on follow-up and did not differ by schedule-type.

CONCLUSIONS

Over the 2-year period, adolescents decreased weekly sleep duration and activity, but only those continuing traditional schedules reduced school-night sleep. This suggests greater individual control of school schedule may preserve sleep duration in this age group, which may be beneficial during the transition into adulthood.

Less physical activity and more varied and disrupted sleep is associated with a less favorable metabolic profile in adolescents

Background

Sleep and physical activity are modifiable behaviors that play an important role in preventing overweight, obesity, and metabolic health problems. Studies of the association between concurrent objective measures of sleep, physical activity, and metabolic risk factors among adolescents are limited.

Objective

The aim of the study was to examine the association between metabolic risk factors and objectively measured school day physical activity and sleep duration, quality, onset, and variability in adolescents.

Materials and methods

We measured one school week of free-living sleep and physical activity with wrist actigraphy in 252 adolescents (146 girls), aged 15.8±0.3 years. Metabolic risk factors included body mass index, waist circumference, total body and trunk fat percentage, resting blood pressure, and fasting glucose and insulin levels. Multiple linear regression adjusted for sex, parental education, and day length was used to assess associations between metabolic risk factors and sleep and activity parameters.

Results

On average, participants went to bed at 00:22±0.88 hours and slept 6.2±0.7 hours/night, with 0.83±0.36 hours of awakenings/night. However, night-to-night variability in sleep duration was considerable (mean ± interquartile range) 0.75±0.55 hours) and bedtime (0.64±0.53 hours) respectively. Neither average sleep duration nor mean bedtime was associated with any metabolic risk factors. However, greater night-to-night variability in sleep duration and bedtime was associated with higher total body and trunk fat percentage, and less physical activity was associated with higher trunk fat percentage and insulin levels.

Conclusion

Greater nightly variation in sleep duration and in bedtime and less physical activity were associated with a less favorable metabolic profile in adolescents. These findings support the idea that, along with an adequate amount of physical activity, a regular sleep schedule is important for the metabolic health of adolescents.

Less screen time and more physical activity is associated with more stable sleep patterns among Icelandic adolescents

OBJECTIVES

Emerging evidence suggests that inconsistent sleep may affect physical and psychological health. Thus, it is important to identify modifiable determinants of sleep variability. Screen time and physical activity are both thought to affect sleep, but studies of their relationship to sleep variability using objective measures are lacking. We examined cross-sectional associations between these variables in mid-teen adolescents using objectively measured sleep and activity.

METHODS

Wrist-worn accelerometers were used to measure one week of sleep and activity in 315 tenth grade students (mean age 15.8y) from six Reykjavík compulsory schools. Participants reported their daily hours of screen time. Regression analysis was used to explore associations of screen time and physical activity with variability in duration, quality, and timing of sleep, adjusting for DXA-measured body fat percentage, parental education, and physical activity or screen time.

RESULTS

Screen time, especially game playing, was associated with variability in duration, timing, and quality of sleep, most strongly with variation in bedtime. Physical activity was inversely associated with variability in duration, timing, and quality of sleep, most strongly with variation in the number of awakenings. Boys had less stable sleep patterns and higher screen time than girls, and sex-specific associations of screen time with sleep variability parameters were significant for boys only.

CONCLUSIONS

Less screen time and more physical activity were independently associated with less sleep variability among mid-teen adolescents. Our results indicate that encouraging youngsters toward an active lifestyle with limited screen use may be important to achieve more consistent sleep.

Training Schedule and Sleep in Adolescent Swimmers

PURPOSE

Insufficient sleep duration may affect athletic performance and health. Inconsistent sleep pattern also has negative health effects, but studies on athletes' intraindividual sleep variability are scarce. The aim of this research was to compare total sleep time (TST) and variability (TST-variability), wakening after sleep onset, and sleep efficiency, during nights preceding early morning practices with other nights, and to investigate sleep characteristics of nights following a day with early morning only, evening only, or both a morning and an evening session in adolescent swimmers.

METHODS

Wrist-worn accelerometers were used to measure 1 week of sleep in 108 swimmers (mean age 16.1 [2.6] y) in Iceland. Adjusted regression analyses and linear mixed models were used to explore associations of training schedules with TST, TST-variability, wakening after sleep onset, and sleep efficiency.

RESULTS

Mean TST was 6:32 (h:min) (±39 min) and TST-variability was 63 minutes (±25 min). TST decreased and TST-variability increased with more early morning practices. TST preceding early training was 5:36 and 5:06 in <16- and ≥16-year-olds, respectively, shorter than on nights preceding later or no morning training (P < .001).

CONCLUSION

Swimmers have extremely short TST preceding early morning sessions and increased TST-variability with more early morning sessions.

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.

[Physical activity and sleep in Icelandic adolescents]

INTRODUCTION

Physical activity and sleep are major determinants of overall health. According to international recommendations, adolescents should engage in moderate to vigorous physical activity for at least 60 min each day and sleep eight to ten hours each night. The association between physical activity and sleep in adolescents is not well known. The aim of the study was to estimate a) the proportion of Icelandic adolescents that achieves recommended physical activity and sleep, b) if there is an association between physical activity and sleep patterns, and c) sex differences in physical activity and sleep.

MATERIAL AND METHODS

A total of 411 adolescents from the 10th grade in six schools in Reykjavik were invited to participate in a cross-sectional study in the spring of 2015. Valid data was obtained from 106 boys and 160 girls. Objective and subjective measures of physical activity and sleep were made by wrist-worn accelerometers and a questionnaire.

RESULTS

Almost half of the participants fulfilled the physical activity recommendations according to the questionnaire. Although 51.1% reported usually getting enough sleep, only 22.9% achieved the recommended sleep length according to objective assessment. No associations were observed between sleep and subjective physical activity. Girls had higher accelerometer-measured physical activity than boys on non-school days (p<0.01), but weekly averages were not different between sexes. Girls and boys did not differ in subjective or objective measures of sleep.

CONCLUSION

The behavior of Icelandic adolescents does not reflect recommended amount of sleep and physical activity. Only 22.9% obtained the recommended sleep length and just 11.3% fulfilled recommendations of both sleep and physical activity.

One-year longitudinal study found a bidirectional relationship between physical activity and sleep disturbance in teenage Estonian girls

AIM

Studies have shown that physical activity (PA) declines during adolescence, especially among girls. We examined the relationship between changes in PA and sleep disturbance over the school year, including any bidirectional relationship between these two factors.

METHODS

This longitudinal study focused on teenage girls who had a mean age of 13.2 ± 0.2 years at baseline and were drawn from five randomly selected schools in the city of Tartu, Estonia. Sleep disturbance and PA were subjectively assessed using the self-reported 3-Day Physical Activity Recall and the Insomnia Severity Index, at four intervals throughout the school year. Latent growth curve analyses were used to evaluate longitudinal and bidirectional associations between PA and sleep disturbance.

RESULTS

Assessments were carried out at three-monthly intervals from September 2015 to June 2016. Sleep disturbance increased and PA decreased over the school year. At baseline, higher PA was related to lower sleep disturbance. Changes in PA corresponded with changes in sleep disturbance. A negative bidirectional relationship between sleep disturbance and PA over time was also found.

CONCLUSION

These results suggest that increasing PA may be an effective strategy for improving the quality of sleep in girls during early adolescence.

High school start times after 8:30 am are associated with later wake times and longer time in bed among teens in a national urban cohort study

OBJECTIVES

High school start times are a key contributor to insufficient sleep. This study investigated associations of high school start times with bedtime, wake time, and time in bed among urban teenagers.

DESIGN

Daily-diary study nested within the prospective Fragile Families and Child Wellbeing Study.

SETTING

Twenty US cities.

PARTICIPANTS

Four hundred thirteen teenagers who completed ≥1 daily diary report on a school day.

MEASUREMENTS

Participating teens were asked to complete daily diaries for 7 consecutive days. School-day daily diaries (3.8±1.6 entries per person) were used in analyses (N=1555 school days). High school start time, the main predictor, was categorized as 7:00-7:29 am (15%), 7:30-7:59 am (22%), 8:00-8:29 am (35%), and 8:30 am or later (28%). Multilevel modeling examined the associations of school start times with bedtime, wake time, and time in bed. Models adjusted for age, sex, race/ethnicity, household income, caregiver's education, and school type.

RESULTS

Teens with the earliest high school start times (7:00-7:29 am) obtained 46 minutes less time in bed on average compared with teens with high school start times at 8:30 am or later (P<.001). Teens exhibited a dose-response relationship between earlier school start times and shorter time in bed, primarily due to earlier wake times (P<.05). Start times after 8:30 am were associated with increased time in bed, extending morning sleep by 27-57 minutes (P<.05) when compared with teens with earlier school start times.

CONCLUSION

Later school start times are associated with later wake times in our large, diverse sample. Teens starting school at 8:30 am or later are the only group with an average time in bed permitting 8 hours of sleep, the minimum recommended by expert consensus for health and well-being.

Computing sleep deficiency

Sleep deficiency is a major public health concern. Since epidemiological studies play an important role in public health evaluations, this theoretical paper pursues answers to the question: 'How can we compute sleep deficiency as informative measures of exposures or doses in observational research?' Starting from the social jetlag concept and based on the chronodisruption rationale, we illustrate and discuss five approaches (one established and four untested, each with unique strengths and limitations) to quantify sleep deficiency by focusing on the timing and duration of sleep. Hitherto, social jetlag and chronodisruption rationale were neither explicitly proposed nor developed as assessments of sleep deficiency but, as we suggest, could potentially be utilized to this end. This first foray into computing sleep deficiency in epidemiological studies makes clear that laboratory, field and epidemiological collaboration is pre-requisite to elucidating potential (co-)causal roles of sleep deficiency in disease endpoints.