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Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials

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Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials

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An article exploring the relationship between sleep quality and mental health conditions.

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Summary

The extent to which sleep is causally related to mental health is unclear. One way to test the causal link is to evaluate the extent to which interventions that improve sleep quality also improve mental health. We conducted a meta-analysis of randomised controlled trials that reported the effects of an intervention that improved sleep on composite mental health, as well as on seven specific mental health difficulties. 65 trials comprising 72 interventions and N = 8608 participants were included. Improving sleep led to a significant medium-sized effect on composite mental health ( g+ = −0.53), depression ( g+ = −0.63), anxiety ( g+ = −0.51), and rumination ( g+ = −0.49), as well as significant small-to-medium sized effects on stress ( g+ = −0.42), and finally small significant effects on positive psychosis symptoms ( g+ = −0.26). We also found a dose response relationship, in that greater improvements in sleep quality led to greater improvements in mental health. Our findings suggest that sleep is causally related to the experience of mental health difficulties. Future research might consider how interventions that improve sleep could be incorporated into mental health services, as well as the mechanisms of action that explain how sleep exerts an effect on mental health.

Keywords: Sleep, Insomnia, CBTi, Mental health, Meta-analysis, Causal inference, Depression, Anxiety, Stress, Psychosis

Does improving sleep lead to better mental health? A meta-analysis of randomised controlled trials

Problems sleeping are common. A review of several hundred epidemiological studies [ 1] concluded that nearly one-third of the general population experience symptoms of insomnia (defined as difficulties falling asleep and/or staying asleep), between 4% and 26% experience excessive sleepiness, and between 2% and 4% experience obstructive sleep apnoea. Additionally, a recent study of over 2000 participants reported that the prevalence of 'general sleep disturbances' was 32% [ 2] and Chattu et al. concluded on the basis of a large systematic review of the evidence that public and health professionals need to be more aware of the adverse effects of poor sleep [ 3]. Mental health problems are also common, with around 17% of adults experiencing mental health difficulties of varying severities [ 4], and evidence from large nationally representative studies suggesting that mental health difficulties are on the increase [ 5]. Sleep and mental health are, therefore, global public health challenges in their own right, with each having substantive impacts on both individuals and society [ 3, 6, 7]. However, problems sleeping and mental health difficulties are also intrinsically linked [ 8, 9]. It was previously assumed that mental health difficulties led to problems sleeping [ 10, 11]; however, the reverse may also be true [ 12], such that poor sleep contributes to the onset, recurrence, and maintenance of mental health difficulties [[13]∗, [14], [15]∗, [16], [17]]. Therefore, the extent to which there is a causal relation between (poor) sleep and (worse) mental health and the possibility that interventions designed to improve sleep might be able to reduce mental health difficulties warrants investigation.

Evidence on the relationship between sleep and mental health

The association between sleep and mental health is well documented [ 9, 13,[18], [19], [20], [21], [22], [23]∗]. For example, people with insomnia are 10 and 17 times more likely than those without insomnia to experience clinically significant levels of depression and anxiety, respectively [ 24]. Furthermore, a meta-analysis of 21 longitudinal studies reported that people with insomnia at baseline had a two-fold risk of developing depression at follow-up compared with people who did not experience insomnia [ 13]. Although research most commonly studies the associations between insomnia and depression and anxiety, there is also evidence that problems sleeping are associated with a variety of mental health difficulties. For example, poor sleep has also been associated with post-traumatic stress [ 25], eating disorders [ 26], and psychosis spectrum experiences such as delusions and hallucinations [ 23, 27]. Studies have also found that specific sleep disorders, such as sleep apnoea [ 28], circadian rhythm disruption [ 29], restless leg syndrome [ 30], excessive daytime sleepiness and narcolepsy [ 31, 32], sleepwalking [ 33], and nightmares [ 34] are all more prevalent in those experiencing mental health difficulties.

Unfortunately, most research on the association between sleep and mental health is observational in design. While informative, inferring causation from such studies is difficult. For example, cross-sectional designs tell us that variables are associated in some way, but they cannot say whether one variable precedes the other in a causal chain [ 35]. Longitudinal designs provide stronger evidence, but are prone to residual confounding [[36], [37], [38]] and other forms of bias that limit causal inference [[39], [40], [41], [42], [43]]. The best evidence is provided by studies that randomly allocate participants to experimental and control conditions to minimise the effects of potential confounds [ 44, 45]. Therefore, to establish whether sleeping problems are causally associated with mental health difficulties, it is necessary to experimentally manipulate sleep to see whether changes in sleep lead to changes in mental health over time (i.e., the interventionist approach to causation, [ 46]).

Many RCTs have examined the effect of interventions designed to improve sleep (typically cognitive behavioural therapy for insomnia, CBTi), on mental health (typically depression and anxiety). There have also been attempts to meta-analyse some of these RCTs and quantify their effects on mental health outcomes [[47]∗, [48], [49], [50]]. However, even these meta-analyses do not permit robust conclusions as to the causal impact of sleep on mental health outcomes for several reasons. First, previous reviews have included studies that did not successfully manipulate sleep (i.e., the intervention did not improve sleep relative to controls). It is not possible to conclude whether sleep is causally linked to mental health if the experimental manipulation of sleep is unsuccessful [ 51]. Indeed, these studies simply tell us that it can sometimes be difficult to improve sleep in the first place. Second, reviews have tended to examine the effect of interventions targeting sleep on mental health at the first post-intervention time point. This is problematic for two reasons; 1) there is no temporal lag between the measurement of sleep and measurement of mental health (a key tenet of causal inference); and 2) effects are limited to the short-term where they are likely to be strongest. Third, the focus of previous reviews has been limited to depression and anxiety only, and typically limited to CBTi interventions. Therefore, the effect of improving sleep on other mental health outcomes, using different approaches to intervention, is limited. Finally, to date there has been no or limited attempts to investigate variables that influence - or moderate - the impact of interventions that improve sleep on mental health. It is crucial that the impact of such variables is systematically examined to understand whether the effect of improving sleep on mental health differs across populations, settings, and study designs.

The present review: an interventionist approach to causation

The present review sought to address these issues to provide an accurate and robust estimate of the effect of changes in sleep quality (i.e., as a result of an intervention) on changes in mental health. To test this empirically, we identified randomised controlled trials that successfully manipulated sleep in an intervention group relative to controls, and then measured mental health at a later follow-up point. We did not limit the scope of interventions to CBTi, or the measures of mental health to solely depression and/or anxiety. Instead, we included any intervention designed to improve sleep that produced a statistically significant effect on sleep quality relative to controls and examined the effect of that improvement in sleep on any subsequent mental health outcome. To better isolate the effect of improved sleep on mental health, we excluded interventions that included specific elements targeting mental health (e.g., CBT elements for depression). Given the (potentially) high degree of heterogeneity between studies that this approach might create, we examined the effect of different study characteristics and outcomes using moderation analyses. Our primary hypothesis is that interventions that significantly improve sleep will lead to significantly improved mental health at follow-up.

Method

Eligibility criteria

To be included in the present review, studies needed to 1) be a randomized controlled trial that tested an intervention designed to improve sleep; 2) produce a statistically significant effect on sleep quality when compared to a control group or an alternative treatment, 3) report a measure of mental health subsequent to the measure of sleep quality, 4) report sufficient data to compute an effect size representing the impact of the intervention on both sleep quality and mental health, 5) be written in English, or translatable using available resources. In order to reliably and validly assess the independent contribution of changes in sleep on mental health outcomes among adult populations, studies were excluded if 1) the intervention contained elements that specifically target a mental health problem in addition to elements that target sleep; or 2) recruited children and young people (i.e., <18 years of age).

Search strategy

First, we searched MEDLINE (1946 to present), Embase (1974 to present), PsycINFO (1967 to present), and The Cochrane Library (1898 to present) using the Cochrane Highly Sensitive Search Strategy (i.e., HSSS, [ 52]) to identify RCTs that included terms relating to sleep quality and/or sleep disorders, and mental health (see for a list of the search terms and Supplementary Material 1 for an example search strategy). Second, the reference lists of extant reviews of the relationship between sleep and mental health were searched for any potential articles. Third, a search for any unpublished or ongoing studies was conducted by searching online databases including White Rose Online, The National Research Register, WHO approved clinical trial databases (e.g., ISRCTN), and PROSPERO. Searches were originally conducted in May 2019 and then updated in February 2021.

Data management and study selection

We followed PRISMA guidelines [ 53] when selecting studies. The first phase of screening removed duplicate records and records that were clearly ineligible based on the title and/or abstract. The second phase of screening cross-referenced full-text versions of articles against the inclusion criteria, with eligible records included in the present review, and ineligible records excluded along with reasons for exclusion. Records were screened by two members of the review team, and a sub-sample of 10% of each reviewer's records were second checked by the other reviewer, with almost perfect agreement between the reviewers ( kappa = 1.00 and 0.99).

Data extraction

Data was extracted from included studies using a standardized form and an accompanying manual detailing each variable for extraction. In addition to extracting statistical data to compute effect sizes, data pertaining to source characteristics of included studies (e.g., publication status, year, impact factor), characteristics of the sample (e.g., age, type of mental health problem), the study (e.g., the nature of the comparison group, length of follow-up), and the intervention (e.g., intervention type, mode of delivery) was also extracted.

Outcomes and prioritization

Measuring improvements in sleep

The concept of 'improved sleep' is multifaceted and can mean different things to different people [[54], [55], [56]]. Consequently, one challenge for the proposed review was to ensure that included studies assessed a similar notion of improved sleep so that they could be meaningfully combined using a single metric. Therefore, we specified that primary studies reported a measure that reflected the overall quality of sleep experienced by participants. The concept of sleep quality can also be subjective [ 54]; however, broadly speaking, sleep quality consists of sleep continuity (e.g., sleep onset, sleep maintenance, and number of awakenings) and daytime impact (e.g., the extent to which the person feels refreshed on waking and throughout the day, see [ 54, 57]). We used the following hierarchy to decide which outcome measure(s) to use to estimate an effect size (in descending order of prioritization); 1) self-report measures of global sleep quality (e.g., the Pittsburgh Sleep Quality Index); 2) outcomes specific to a given sleep disorder that assess sleep continuity and impact on daily life (e.g., the Insomnia Severity Index); and 3) individual components of self-reported sleep continuity aggregated to form a single composite effect size (e.g., the average effect of intervention on sleep onset latency (SoL) and wake after sleep onset (WASO)).

Measuring mental health

We examined the effect of improving sleep on 1) composite mental health (which included all mental health outcomes reported across studies, see for outcomes), and 2) specific mental health difficulties in isolation (e.g., depression separately from other mental health outcomes). We computed the between-group effect of improving sleep on each mental health outcome reported by the study at the furthest follow-up point available. This strategy provides a stringent test of the effect of improving sleep on mental health outcomes in the sense that any changes need to have been maintained over time. In line with previous reviews [ 58], these effect sizes were then averaged to form a 'composite' measure of mental health. As with the measures of sleep quality, we prioritized self-report measures of mental health rather than observer-rated measures, as arguably it is the subjective experience of mental health problems that is most important [ 59].

Risk of bias

Risk of bias was assessed using the risk of bias assessment criteria developed by the Cochrane Collaboration [ 60]. RCTs were classified as being at overall risk of bias according to three of the six domains - 1) allocation concealment, 2) blinding of outcome assessment and 3) completeness of outcome data (attrition). RCTs judged as being at low risk of bias for all three domains were judged at overall low risk of bias. RCTs judged as being at high risk for any of the three domains were judged as overall high risk of bias. RCTs judged as a mix of low and unclear risk on these three domains, or all unclear were judged as unclear with respect to risk of bias.

Estimating effect sizes

Hedges g and the associated standard error were estimated using the means and standard deviations reported by each of the primary studies. Where means and standard deviations were not reported, effect sizes were estimated by converting relevant summary statistics into Hedges g. Where studies reported multiple outcome measures for the same/similar constructs (e.g., several measures of depression), effect sizes were computed for each outcome and then meta-analysed in their own right to form one overall effect.

Meta-analytic approach

All analyses were conducted in R [ 61], using the ' esc' [ 62], ' meta' [ 63], ' metafor' [ 64], ' dmetar' [ 65], and ' robvis' [ 66] packages. The pooled, sample-weighted, average effect size was computed using a random effects model as effect sizes between studies are likely to vary considerably [ 67]. Following Cohen's recommendations [ 68], g = 0.20 was taken to represent a 'small' effect size, g = 0.50 a 'medium' effect size and g = 0.80 a 'large' effect size. The I ² statistic was used to assess heterogeneity of effect sizes across the included studies and was interpreted according to the classifications suggested by Higgins et al. [ 69], where I ² = 25% indicates low heterogeneity, I ² = 50% indicates moderate heterogeneity, and I ² = 75% indicates high heterogeneity. Publication bias was assessed via visual inspection of a funnel plot and Egger's test [ 70]. Additionally, Orwin's formula [ 71] was used to determine the fail-safe n. Finally, outliers were defined as any effect size for which the confidence intervals did not overlap with the confidence interval of the pooled effect [ 72]. We conducted a sensitivity analysis examining the effect of outliers for each outcome by rerunning the analysis with any outlying effect sizes removed.

Subgroup analyses

Moderation analysis was conducted to identify variables that were associated with the effect of improving sleep on mental health outcomes. A minimum of three studies representing each moderator level category was required in order to conduct moderation analysis. For categorical variables, the analysis was based on a mixed effects model, in that the pooling of effect sizes within each moderator level was based on a random effects model, while the comparison of effect sizes between levels was based on a fixed effects model. The Q statistic was then used to assess whether effect sizes were significantly different between moderator levels. For continuous variables, sample-weighted meta-regression was used to investigate the impact of the moderator on mental health effect sizes.

Data availability statement

All data and analysis code are freely available on the Open Science Framework under a creative commons 4.0 license (for access, see [ 73]).

Results

Study selection

shows the flow of records through the review. Systemic searches of the published and grey literature retrieved a total of 21,733 records, which was reduced to 15,139 after duplicates were removed. Of these records, 14,687 (97%) were excluded in the first stage of screening, leaving 452 full-text records to be screened. Of these records, 387 (86%) were cross-referenced against the review eligibility criteria and excluded (see for a breakdown of reasons and Supplementary Materials 2 for a list of the studies excluded at this stage), leaving 65 records for inclusion in the meta-analysis.

Study characteristics

describes key characteristics of the included studies. The 65 studies provided 72 comparisons between an intervention that successfully improved sleep quality vs. a control group.

Participants

A total of N = 8608 participants took part across the 72 interventions. 38 of the comparisons (53%) included participants with a comorbid physical or mental health problem, while 31 (43%) reported no comorbid health problems, and 3 (4%) reported insufficient detail to make a judgement. Of the 38 comparisons including participants with comorbid health problems, 18 (47%) reported mental health diagnoses, and 20 (53%) had physical health problems.

Outcome measures

The majority of comparisons (61, 85%) reported a measure of depression, but 33 (46%) reported a measure of anxiety, 6 (8%) reported a measure of stress, 5 (7%) reported measures of psychosis spectrum experiences (e.g., total, positive, and negative symptoms), 9 (13%) reported a measure of general mood, 2 (3%) reported post-traumatic stress disorder outcomes, 2 (3%) reported measures of suicidal ideation, 4 (6%) reported rumination outcomes, and 1 (2%) reported a measure of psychological burnout.

Interventions and comparisons

Most interventions were multi-component CBTi (53, 74%), but interventions also involved acupuncture (7, 10%), pharmacological treatments (2, 3%), sleep hygiene alone (2, 3%), sleep restriction alone (2, 3%), Tai Chi (2, 3%), CBT for nightmares (1, 2%), herbal remedies (1, 2%), walking (1, 2%), and yoga (1, 2%). Interventions were most often compared against an active control group (34, 47%), but were also compared to waitlist control groups (25, 35%), and groups receiving treatment as usual (13, 18%). On average participants' mental health was followed-up 20.5 weeks post-intervention (median = 12 weeks post-intervention), with the earliest follow-up being 4-weeks post-intervention, and the furthest follow-up 156-weeks (three years) post intervention.

Manipulation check: did sleep quality improve significantly in the intervention group relative to controls?

Before we examined the effect of improving sleep quality on subsequent mental health, we confirmed that studies included in the review successfully improved sleep quality. The interventions had large and statistically significant effects on sleep quality at the earliest follow-up point reported ( g₊ = −1.07, 95% CI = −1.26 to −0.88, p < 0.001), although heterogeneity between studies was substantial ( I = 79%, Q = 331.93, p < 0.001). After twelve outlying effect sizes were removed, the effect of the interventions on sleep quality remained large and statistically significant ( g₊ = −0.97, 95% CI = −1.07 to −0.88, p < 0.001), and heterogeneity was reduced to moderate levels ( I = 43%, Q = 102.32, p < 0.001). These findings suggest that the primary studies included in the present review successfully manipulated sleep quality, even after accounting for outliers.

What effect do improvements in sleep quality have on mental health?

presents the effect of improving sleep quality on composite mental health outcomes, and on measures of depression, anxiety, stress, psychosis spectrum experiences, suicidal ideation, PTSD, rumination, and burnout.

Post-traumatic stress disorder (PTSD)

Burnout

Only one study reported the effect of improving sleep on burnout finding almost zero effect ( g = −0.03, CI = −0.58 to 0.52, p = 0.917).

Moderators of the effect of improving sleep quality on composite mental health outcomes

presents the findings of analyses evaluating categorical moderators of the effect of improving sleep quality on composite mental health outcomes and presents analyses evaluating continuous moderators using meta-regression. Studies that found significant effects of the intervention on sleep quality reported larger effects on subsequent composite mental health, ( g = −0.53, 95% CI = −0.68 to −0.38, p < 0.001), than studies that did not find a significant effect of the intervention on sleep quality, ( g = −0.12, 95% CI = −0.24 to 0.01, p = 0.0522), a difference that was statistically significant, ( Q = 17.59, p < 0.001). This finding strengthens the notion that improvements in sleep are behind improvements in mental health. The effect of improving sleep on mental health was larger in studies with shorter follow-up periods, (i.e., <6 months, g+ = −0.60), than in studies with longer follow-ups, (i.e., 6 months, g+ = −0.18, Q = 10.75, p < 0.01). Furthermore, interventions that were delivered face-to-face by a clinician or therapist were associated with significantly larger effects on mental health, ( g+ = −0.63), than those that were self-administered by participants, ( g+ = −0.34, Q = 4.50, p < 0.05). Finally, there was significant variation in the size of the effect between countries ( Q = 53.69, p < 0.001). No other statistically significant categorical moderator effects were found. Regarding continuous moderators, meta-regression revealed a statistically significant dose-response effect for the association between the effect of interventions on sleep quality and the effect on subsequent mental health outcomes ( B = 0.77, 95% CI = 0.52 to 1.02, p < 0.001), suggesting that greater improvements in sleep led to greater improvements in mental health. No other continuous variables significantly moderated the effect of improving sleep on mental health.

Post-hoc moderation analysis

Is the smaller effect of improving sleep on mental health at longer follow-ups associated with smaller effects on sleep quality?

We conducted further (unplanned) post-hoc analysis to investigate whether the smaller effect of improving sleep on mental health at longer follow-ups was accompanied by a reduction in the improvements to sleep quality. Studies reporting the effect of the intervention at shorter follow-ups reported larger improvements in sleep quality, ( g = −1.03, 95% CI = −1.27 to −0.78, p < 0.001), than those reporting longer follow-ups ( g = −0.44, 95% CI = −0.62 to −0.27, p < 0.001), a difference that was statistically significant, ( Q = 14.38, p < 0.001). This suggests that the smaller effect of improving sleep on mental health at longer follow-ups might be driven by a smaller effect of the interventions on sleep quality at longer follow-ups.

Can some of the effect of improved mental health be explained by CBTi modules that target processes associated with mental health?

Finally, although the present review excluded interventions that specifically and directly targeted mental health, some CBTi protocols include modules that might target similar processes associated with some mental health difficulties (rumination around sleep, catastrophizing over the effect of poor sleep etc.). Therefore, we compared CBTi interventions with modules that could target processes associated with mental health vs. interventions that did not include these modules (e.g., sleep restriction alone, sleep hygiene alone, herbal tea, and pharmacological intervention). There were no significant differences in the effect of improved sleep quality on mental health between CBTi interventions including modules addressing processes associated with mental health ( g = −0.44, 95% CI = −0.59 to −0.29, p < 0.001), relative to those that did not ( g = −0.48, 95% CI = −0.65 to −0.32, p < 0.001, Q = 2.51, p = 0.285). This finding suggests that it is the beneficial effect of improved sleep quality that confers improvements in mental health rather than the inclusion of modules that target processes associated with mental health commonly seen in CBTi protocols.

Risk of bias assessments

summarizes the weighted assessment of risk of bias. Individual risk of bias judgements for included studies are presented in Supplementary Material 3. Ten studies (15%) were judged as having low risk of bias, 29 studies (45%) were judged as high risk of bias, and 26 studies (40%) were judged as unclear. The methodological quality of the included studies was not associated with the effect of improving sleep on composite mental health outcomes, Q = 0.72, p = 0.395.

Publication bias

A funnel plot of the effect of improving sleep quality on composite outcomes revealed asymmetry in the effect sizes (Egger's regression = −1.09, 95% CI = −1.91 to −0.28, p < 0.05, see ). Duval and Tweedie's [ 74] trim and fill procedure was therefore used to address the asymmetry. Ten studies were imputed resulting in a statistically significant, small-to-medium sized adjusted effect of improving sleep on composite mental health outcomes ( g₊ = −0.35, 95% CI = −0.55 to −0.16, p < 0.001). Orwin's failsafe n test suggested that an additional 4101 comparisons producing null effects would be needed to reduce the average effect of improving sleep on composite outcomes to zero. Taken together these results suggest that the effect of improving sleep on composite mental health is robust to possible publication bias.

Discussion

The present review used meta-analysis to synthesize the effect of 72 interventions that improved sleep quality relative to a control condition on subsequent mental health. The findings revealed that improving sleep quality had, on average, a medium-sized effect on mental health, including clear evidence that improving sleep reduced depression, anxiety, and stress. A dearth of primary studies of other mental health difficulties (e.g., psychosis spectrum experiences, suicidal ideation, PTSD, rumination, and burnout) mean that it is premature to draw definitive conclusions in these areas. It was also notable that we found a dose-response relationship between improvements in sleep quality and subsequent mental health, such that greater improvements in sleep led to greater improvements in mental health. Although there was some evidence of publication bias, the effects remained robust to correction. Taken together, the findings suggest that improving sleep leads to better mental health, therefore providing strong evidence that sleep plays a causal role in the experience of mental health difficulties.

Sleep as a transdiagnostic treatment target

The present findings support the idea that targeting sleep promotes mental health across a range of populations and experiences. The effect of improving sleep quality on composite mental health was medium-sized and statistically significant, regardless of the presence of physical and/or mental health comorbidities. This finding is particularly important given the healthcare challenges associated with multimorbidity [ 75] and mental and physical health problems often co-occur [[76], [77], [78], [79]], something that appears to be increasing [ 80]. Consequently, it is important that the benefits of improving sleep on mental health occur even in the presence of comorbid health complaints, as was reported in the present research. Improving sleep has also been shown to improve aspects of physical health, including fatigue [ 81], chronic pain [ 82, 83], and overall health related quality of life [ 84] and could reduce the cost of healthcare. For example, offering a digital CBTi intervention (Sleepio) to primary care patients was associated with an average saving of £70.44 per intervention user [ 85], and cost savings following sleep intervention have also been specifically reported in people with comorbid mental health difficulties such as depression [ 86].

Another finding to suggest that targeting sleep could promote mental health across a range of populations and experiences, is that we found no difference in the effect of improving sleep quality on mental health between those with clinically defined mental health difficulties and those with non-clinical experiences or between those recruited from clinical vs. community settings, with both groups receiving significant benefits of improved sleep on mental health. This suggests that improving sleep could prove helpful across a range of mental health severities, thus broadening the possible impact of sleep interventions within healthcare services. Finally, there is growing evidence that sleep disturbances predict the development of mental health difficulties in the future. For example, shorter and more variable sleep has been shown to be longitudinally associated with more severe hallucinations and delusional ideation in those at high-risk of psychosis [ 87]. The present research found that improving sleep has a significant beneficial impact on future mental health in those with non-clinical experiences, raising the possibility that delivering interventions that improve sleep early might limit the risk of developing (or exacerbating) substantive mental health difficulties. Indeed, less severe mild-to-moderate presentations of mental health difficulties can develop over time into more severe mental health diagnoses [ 88, 89], therefore improving sleep might be one tool that can be used in combination with others to limit the risk of transition.

Strengths and limitations

The present review has several strengths. First, it provides a comprehensive and up-to-date search of RCTs examining the effect of improving sleep on a variety of subsequent mental health outcomes. Indeed, with 65 RCTs and N = 8608 participants, the present review is one of the largest studies of the effect of improving sleep on mental health to date. Second, the review was specifically designed to test the causal association between sleep and mental health (i.e., RCTs only, successful sleep improvement required, temporal lag between measures etc.). To our knowledge, the review is the first to adopt this approach in the field of sleep and mental health, although the general approach has been used in other fields [ 90]. Finally, we provide an analysis of possible moderators of the effect of improving sleep on mental health, identifying several key moderators of the effect.

However, there are limitations that must be considered when interpreting the findings. First, relatively few studies examined the effect of improving sleep over the long term. Those that did report longer follow-ups generally found smaller effects (although still statistically significant), most likely due to the diminishing effects of interventions on sleep quality over time [ 91]. Consequently, it is important that interventions targeting sleep quality as a route to improving mental health seek to maintain their beneficial effects. Second, there were few primary studies for some of the outcomes included in this review. Consequently, in lieu of more studies reporting these outcomes, the inferences that we can make for mental health outcomes other than depression and anxiety are more limited. Third, although the intention of the present review was to include a broad range of sleep disturbances, most of the analyses are based on CBT interventions for insomnia. This might be due to the relationship between insomnia and mental health being the one that is historically most studied. However, it may be that our focus on sleep quality precluded some studies that do not focus on insomnia from inclusion. For example, different sleep disorders have different conceptualisations of improvement that might not include sleep quality. For example, the timing of sleep is particularly important in circadian rhythm disorders and daytime sleepiness is a key outcome in sleep apnoea research. Future research might consider examining the effect of improving specific sleep disorders on mental health by conceptualising improvements using sleep disorder specific outcomes.

Future directions

The present review highlighted several areas for future research in terms of both research and theory, and the implementation of findings in practice. First, given that mental health was measured on average around 20.5 weeks post-intervention in the primary studies, and that the effect of improving sleep on mental health significantly reduced over time, future research should examine the effect of improving sleep on mental health over the longer term. Second, although not uncommon, the majority of RCTs included in the present review were at high risk, or unclear risk or bias. Consequently, in addition to studying the effect of improving sleep over the longer term, on a range of mental health difficulties beyond depression and anxiety, we need more research at lower risk of methodological bias.

Finally, although the present research provides evidence for a causal association between sleep and mental health, it is less clear how sleep affects mental health. One potential mechanism is whether and how people regulate their emotions (e.g., in response to negative events). Indeed, evidence suggests that poor sleep can amplify the adverse effect of negative life events [ 92, 93], dull the beneficial impact of positive events [ 94], and is associated with more frequent use of emotion regulation strategies that might be detrimental to good mental health [ 95]. By extension, although we are unaware of RCTs testing the effect of improved sleep on emotion regulation, changes in sleep are prospectively associated with changes in aspects of emotion regulation [ 96, 97], while experimentally induced sleep deprivation is adversely linked to poorer emotion regulation [ 96, 97]. Contemporary perspectives on emotion regulation (e.g., the action control perspective), draw on research on how people regulate their behaviour, to propose that regulating emotions involves three tasks, 1) identifying the need to regulate, 2) deciding whether and how to regulate, and 3) enacting a regulation strategy [ 98]. We propose that poor sleep quality has the potential to adversely affect anyone (or all) of the three tasks involved in effectively regulating emotions, which might go some way toward explaining the relationship between poor sleep and mental health. Therefore, we would recommend that future research includes measures of aspects of emotion regulation (e.g., the Difficulties in Emotion Regulation Scale, [ 99]) within experimental and longitudinal designs to elucidate possible mechanisms by which improvements in sleep benefit mental health.

In terms of practice and implementation, evidence on the effect of sleep on mental health also supports calls for routine screening and treatment of problems with sleep. Both the Royal Society for Public Health (RSPH) and the Mental Health Foundation (MHF) recommend that primary health care training should include awareness of, and skills in assessing, sleep problems [ 100, 101]. Despite this and a growing body of evidence, there has been little progress to date [ 102]. This may reflect under-appreciation of the importance of sleep [ 103] and lack of training and skills in assessing and managing sleep problems [[104], [105], [106], [107], [108]], as well as limited time and resources [ 103, 109]. Therefore, a profitable next step might be to explore barriers and facilitators to assessing sleep and delivering effective interventions in specific care settings, from both the patient and clinician perspective. Indeed, the present review also highlighted a dearth of trials that tested the effect of improving sleep on mental health outcomes in 'real world' settings (e.g., within existing clinical and community health services). Although some researchers are taking important steps in this area [[110], [111], [112]], there is a clear need for more trials of interventions in clinical services so that the effectiveness and implementation of such interventions in routine care can be better understood.

Conclusions

Taken together, the present research supports the view that sleep is causally related to the experience of mental health difficulties, and therefore that sleep represents a viable treatment target that can confer significant benefits to mental health, as it has been found to do for physical health. We found that improving sleep was associated with better mental health regardless of the severity of mental health difficulty (i.e., clinical vs. non-clinical) or the presence of comorbid health conditions. Poor sleep is almost ubiquitous within mental health services [ 102, 108, 113, 114], is causally related to the experience of mental health difficulties, and represents a potential treatment target [ 105, 115, 116]. Consequently, equipping health professionals with greater knowledge and resources to support sleep is an essential next step. Future research should consider how interventions that improve sleep could be better incorporated into routine mental health care, as well as the possible mechanisms of action that might explain how sleep exerts its effects on mental health.

To fully harness the effect of improved sleep on mental health, it is important that future research:

Explores the barriers and possible solutions to incorporating interventions that improve sleep into mental health care services.

Tests the effect of improving sleep on mental health outcomes beyond depression and anxiety, and over the long term, using designs at low risk of methodological bias.

Investigates the possible mechanisms of action that might explain how sleep exerts its effects on the experience of mental health difficulties.

Sleep is causally related to the experience of mental health difficulties and represents a viable transdiagnostic treatment target for those experiencing mental health difficulties.

Improving sleep has beneficial effects on the experience of mental health difficulties, regardless of the severity of those difficulties, or the presence of comorbid health conditions.

Healthcare professionals aiming to improve mental health (particularly depression, anxiety, and stress) should consider interventions designed to improve sleep, particularly cognitive behavioral therapy for insomnia where the evidence base is strongest.

Funding

This research was funded by the National Institute for Health Research under its Research for Patient Benefit (RfPB) Programme (Grant Reference Number PB-PG- 0817-20027). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Acknowledgements

We thank Anthea Sutton (Information Resources Group Manager, The University of Sheffield) for her help developing the systematic search strategy and managing the records.

Appendix ASupplementary data

The following are the Supplementary data to this article:

3. Chattu V.K., Manzar M.D., Kumary S., Burman D., Spence D.W., Pandi-Perumal S.R. Healthcare. Multidisciplinary Digital Publishing Institute; 2019. The global problem of insufficient sleep and its serious public health implications. [[ PMC free article] [ PubMed] Google Scholar]

60. Higgins J.P., Altman D.G., Sterne J.A. Cochrane handbook for systematic reviews of interventions version 5.1. 0 [updated March 2011] Cochrane Collab; 2011. Chapter 8: assessing risk of bias in included studies. [ Google Scholar]

66. McGuinness L.A., Higgins J.P.T. Risk-of-bias VISualization (robvis): an R package and Shiny web app for visualizing risk-of-bias assessments. Res Synth Methods [Internet] 2020 Apr 26 doi: 10.1002/jrsm.1411. [cited 2020 May 21];n/a(n/a). Available from: [[ PubMed] [ CrossRef] Google Scholar]

73. Scott A., Webb T.L., Martyn-St James M., Rowse G., Weich S. 2021. Supplementary materials for "improving sleep quality leads to better mental health: a meta-analysis of randomised controlled trials." [Internet] Available from: osf.io/6edjv. [[ PMC free article] [ PubMed] Google Scholar]

85. Sampson C.J., Bell E., Cole A., Miller C.B., Rose J. Digital cognitive behavioural therapy for insomnia and primary care costs in England: an interrupted time series analysis. medRxiv. 2021 [[ PMC free article] [ PubMed] Google Scholar]

126. Chao L.L., Kanady J.C., Crocker N., Straus L.D., Hlavin J., Metzler T.J., et al. Cognitive behavioral therapy for insomnia in veterans with gulf war illness: results from a randomized controlled trial. Life Sci. 2021:119147. [[ PMC free article] [ PubMed] Google Scholar]

171. Sheaves B., Freeman D., Isham L., McInerney J., Nickless A., Yu L.M., et al. Stabilising sleep for patients admitted at acute crisis to a psychiatric hospital (OWLS): an assessor-blind pilot randomised controlled trial. Psychol Med. 2017:1-11. [[ PMC free article] [ PubMed] Google Scholar]

# data[].attributes.attachment_link.title
Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials

# data[].attributes.attachment_link.description
The extent to which sleep is causally related to mental health is unclear. One way to test the causal link is to evaluate the extent to which interventions that improve sleep quality also improve mental health. We conducted a meta-analysis of randomised ...

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Sleep in Adolescents: The Role of Sleep in Academic Performance and Well-being

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Sleep in Adolescents: The Role of Sleep in Academic Performance and Well-being

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Discusses the crucial role of sleep in adolescents and its impact on their academic and psychological well-being.

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Abstract

The perfect storm metaphor applies to sleep patterns of adolescents in the sense that developmental trajectories of biopsychosocial factors conspire to limit the quantity of sleep for many adolescents resulting in a number of negative consequences. A reduction in sleep amount from late childhood through the second decade has long been known; however, the weight of current evidence holds that sleep need does not decline across this span. Nevertheless, parents, pediatricians, and school teachers, it seems, long assumed that this sleep decline was an inevitable part of growing up and a normative expectation. We shall see below that the loss of sleep through adolescence is not driven by lower need for sleep but arises from a convergence of biological, psychological, and socio-cultural influences.

Keywords: adolescence, circadian rhythms, sleep regulation, homeostatic pressure, melatonin, mood

Sleep Patterns of Adolescents

Adolescent sleep patterns have been surveyed by investigators in many countries from virtually every continent around the world, and a consistent finding is that the timing of bedtime on school nights gets later across the middle school and high school years (roughly ages 11 through 17 years). Rise times on school mornings, by contrast, tend to stay relatively consistent except in countries such as the United States where the starting time of school moves to an earlier hour at the transition to high school. Weekend sleep for teenagers tends to delay further, and the difference in amount of sleep reported for school days versus weekends becomes more pronounced as children pass into higher grades (i.e., greater reported sleep on weeknights than school nights).

The most recent US poll of sleep patterns in adolescents was reported by the National Sleep Foundation in 2006, and collected self- and parent-reported sleep patterns from grades 6 through 12. These data serve as a good example of these general trends, as shown in . The young people interviewed in this telephone poll reported that average bedtime on the nights before school days were approximately 1.5 hours later from grade 6 to grade 12, and reported weekend bedtime bedtime delayed from 2231 to 0045 across this same grade span. Sixth graders reported going to bed about an hour later on weekend nights, and for twelfth graders, the weekend bedtime delay was about an hour and 45 minutes. The average reported rise time on school mornings was 0642 in grade 6 and 0631 in grade 12. The reported number of hours slept on school nights declined from 8.4 hours in the sixth grade students to 6.9 hours in the twelfth graders; reported weekend sleep was more consistent from grades 6 through 11-about 9 hours-falling to 8.4 hours in grade 12. The weekend extension of sleep time in this report was nearly an hour in the middle school children (grades 6-8) and approached 2 hours for grade 11 students.

Table 1

Amounts of sleep reported by adolescents varies across countries and regions; however, the overall patterns of later sleep timing and diminished sleep across adolescence is reported by most investigators. Reports of Korean youth, for example, indicate they begin and end grades 6 to 12 with later school night bedtimes and less sleep than those in the US. Children were assessed with a survey administered in the classroom querying usual sleep schedule. On average, the children reported:

school night bedtime: grades 5-6 = 2242, grades 7-8 = 2312; grades 9-10 = 0000; grades 11-12 = 0054;

school morning rise time: grades 5-6 = 0718; grades 7-8 = 0700; grades 9-10- = 0648; grades 11-12 = 0618;

school night hours slept: grades 5-6 = 8.3; grades 7-8 = 7.6; grades 9-10- = 6.6; grades 11-12 = 5.4;

weekend night bedtime: grades 5-6 = 2300, grades 7-8 = 2330; grades 9-10 = 2336; grades 11-12 = 2354;

weekend morning rise time: grades 5-6 = 0806; grades 7-8 = 0854; grades 9-10- = 0930; grades 11-12 = 0918;

weekend night hours slept: grades 5-6 = 9.0; grades 7-8 = 8.8; grades 9-10- = 9.0; grades 11-12 = 8.4.

An apparent difference in the older Korean children from the 11 th and 12 th graders in the US poll, was that the reported weekend bedtime was earlier than on school nights. The authors note that many students (71.1%) took additional course work in the evening, with classes lasting until midnight or later for 54.5% of the older students. This pattern of late night classes on school days likely also explains why the Korean high school students reported earlier weekend than school-night bedtime on average. We note as well that the standard deviation for reported weekend bedtime was 3 to 4 hours, much greater than for other variables.

The issue of determining the'sleep need' of adolescents is challenging, since the definition of sleep need itself is in dispute. Early work by Carskadon and colleagues started from a questionnaire assessment of school children in which the discrepancy between school day and weekend reported amount slept was zero, and the average amount was 10 hours a night. Of interest is the parallel finding of Iglowstein and colleagues showing a concurrence of weekend and weekday sleep amounts at about 10 hours from reports by mothers of Swiss children (see in). In the longitudinal study of adolescents by Carskadon, time in bed was fixed at 10 hours a night, and the data showed that youngsters across the span of ages 10 to 17 slept about 9 hours and 20 minutes. The younger children were more likely to waken spontaneously in the lab, whereas the older children took a bit longer to fall asleep and were rarely spontaneously awake at the end of the 10 hours. Furthermore, assessment with the multiple sleep latency test (MSLT, an objective measure of sleepiness) showed that the younger children were more alert across the day, whereas the pubertal and postpubertal adolescents showed a midday trough of alertness in spite of the same amount of nocturnal sleep. In another study that may bear on the issue of sleep need of adolescents, Carskadon and colleagues showed that adolescents (ages 11 to 14.6 years) given a 10-hour sleep schedule for 10 to 14 nights and then studied in the lab for three consecutive 18 hour nights, slept nearly 12.5 hours on average the first night and by night 3 still slept 10.1 hours on average (standard deviation about 1 hour).

Adolescent Development and Sleep Regulation

In describing the biological regulation of sleep, current thinking uses the two-process model, first proposed by Borbély and colleagues. The two processes that comprise the model include a daily (circadian) rhythm of sleep propensity, thought to originate from the suprachiasmatic nucleus of the hypothalamus in mammals, and a sleep-wake 'pressure' (homeostatic) system, for which a neuronanatomical locus has not been identified. In the case of the former process, the signal from the brain's central clock is thought to affect sleep and arousal systems to help gate the timing of sleep. The sleep homeostatic system favors sleep as the hours of wakefulness are extended and favors waking as sleep is prolonged, thus transducing information about the length and amount of prior sleep or wake, rather than the time of day. The role of these two processes can be distinguished independent from one another under certain experimental conditions, but work interactively to regulate sleep from day to day.

Developmental changes in the circadian timing system were inferred by some from the delay in the timing of sleep onset, and two studies published in 1993 gave initial evidence for the hypothesis that a phase delay occurs in association with puberty. For example, Andrade and colleagues found in a longitudinal study that later sleep times occurred for adolescents at a more mature Tanner stage than others. Carskadon and colleagues reported in a crossectional study using self reported sleep and pubertal stage that sixth grade girls who rated themselves more mature had a more evening type score on a measure of circadian phase preference. A subsequent follow-up cross-sectional study in which adolescents' sleep-wake schedules were constrained for two weeks confirmed that circadian phase as evaluated with the objective measure of dim light melatonin onset (DLMO) phase was positively correlated with Tanner stage. In other words, the DLMO was later for participants rated more mature indicating that the onset of the 'biological night' is later for more mature teens.

Several hypotheses have been put forward to explain the features of the circadian timing system that may underlie this developmental change. A review of adolescent changes in sleep and circadian rhythms by Hagenauer and colleagues noted that signs of a juvenile phase delay were found in several mammalian species in addition to humans. highlights these findings. The occurrence of a circadian delay around the time of puberty for a number of species lends credence to the hypothesis that this phenomenon arises from intrinsic biological processes rather than as a response to social/behavioral exposures. Whether and how the phase delay is linked to reproductive development is unknown.

Table 2

If the adolescent phase delay is inherent, what mechanisms might explain the phenomenon. One hypothesis is that circadian period (i.e., the internal day length) may become longer during adolescence. Pubertal male rats were found in one study to have longer intrinsic period than adults, and data from humans show a longer period in teens than in adults. An alternate explanation is that the circadian timing system manifests an altered sensitivity to phase dependent effects of light; in particular, the pacemaker may become more sensitive to light's phase delaying effects during adolescence than in childhood. One animal study that offers support for this hypothesis showed an exaggerated delay of rhythms to light occurring at the phase-delaying time in pubertal than in adult mice. This hypothesis depends on an underlying biological change that is then affected by behavioral exposures that arise during adolescence (i.e., staying awake later, thus experiencing more evening light). A converse to this hypothesis is that adolescents become less sensitive to phase-advancing (morning) light or experience a change in the shape of the phase response curve. Preliminary data from one study of human adolescents provides a bit of evidence supporting this hypothesis.

The influence of the sleep-wake homeostasis system is well understood, although specific neuroanatomical or neurochemical constituents are not yet fully established, nor has a link between adolescent maturation and this physiological process been identified. Thus, whether the homeostatic process might be influenced by hormonal input, physical growth, brain maturation, or other factor(s) is unknown. On the other hand, several evaluations of adolescent sleep physiology provide evidence for developmental changes in this sleep regulatory process. The theoretical model of sleep-wake homeostasis has a long history and a significant amount of experimental data supporting the model, based on sleep slow frequency EEG data from animal and human models (see for review): As waking is prolonged across a day or longer, slow frequency EEG amplitude and incidence increases; as sleep unfolds across the night, slow frequency EEG amplitude and incidence decays. Several studies have shown that adolescent development is not associated with a change in the decay of sleep homeostatic "pressure" (indexed by EEG slow wave activity in sleep) but remains stable across early adolescent development (ages about 9-10 through ages about 14-15 years) and up to age 18 y in another study. On the other hand, preliminary data from a recent analyses of sleep EEG slow wave activity in another adolescent cohort (ages 15-16 through about ages 17-19 years) demonstrate a longer time constant for the decay across the night in several brain regions. One interpretation of the latter findings is that the sleep recovery process is unchanged in early adolescents and that certain parts of the brain may require more sleep to recover in older teens than when younger. This set of findings reinforce the likelihood that sleep need does not decline in adolescents.

Whereas the dissipation of sleep homeostatic pressure may speak to the recovery function of sleep, the other limb of the homeostatic process describes how the 'pressure' to sleep or sleep propensity builds across the waking day. Jenni and colleagues modeled the accumulation of sleep homeostasis in early adolescent humans (ages 9 to 14 y) who were either pre-/early pubertal or post pubertal. This analysis showed a longer time course for the build up of sleep 'pressure' in the more mature group, indicating that the more mature adolescents may find it easier to stay awake longer than the less mature. This hypothesis was confirmed by analyzing speed of falling asleep on the multiple sleep latency test in a group of pre-early pubescent vs. a group of post pubertal adolescents across 36 h of extended waking: the less mature adolescents fell asleep faster than the more mature on the tests occurring 14.5 and 16.5 h after waking.

In summary, current data on the maturation of the biological regulation of sleep during adolescence show that:

the circadian timing system undergoes a phase delay;

the dissipation of sleep homeostatic pressure does not change until late adolescence when it shows evidence of slowing;

the accumulation of sleep homeostastic pressure slows during puberty.

These findings support the notion that the need for sleep is stable (or increases) acrosse adolescent development and that the delay of sleep timing is supported by the circadian shift as well as the slowing of sleep homeostatic pressure accumulation.

Psychosocial Factors Affecting Adolescent Sleep

Many psychosocial factors affect sleep patterns in adolescents and contribute to the phase delay and the decline of time slept. We consider a few examples below and examine how they interact with these biological regulatory processes. A primary psychodevelopmental task of adolescents is to achieve independence in many areas of their lives. One area where this striving for autonomy is displayed is the decision of when to go to sleep. One sleep habits surveys from the early 1980s showed that a significantly higher percentage of children ages 12 or 13 y reported setting their own bedtime on school nights (e.g., 19% of 13 y olds) than did younger children (e.g., 50% of 10 y olds) 10- to 12-y old children and that reported bedtimes were later for the older children. The other end of school-night sleep-waking up- showed an opposite trend: more older children reported needing a parent or alarm to wake them than did the younger children, who were likelier to report that they "just wake up." Thus, the adolescents who actualize autonomy by staying up late seem to "ride the wave" of the circadian phase delay and to avail themselves of the relative emancipation from rapid sleep pressure accumulation. Yet, they were sleeping less and rising early required external intervention to wake up.

Two recent studies highlight the importance of parental-set bedtimes. Gangwisch and colleagues examined from a large epidemiologic data set gathered from adolescents (grades 7 to 12) in the US during the mid-1990s. The analysis showed that young people whose parent set their bedtime at midnight or after relative to those with bedtimes set at 2200 or earlier, were significantly more likely to suffer from depression or suicidal ideation. The authors also found that this association was mediated by total sleep time; thus, those with earlier bedtimes reported sleeping more and were less likely to be depressed or experience suicidal ideation. A smaller, focused study of South Australian adolescents found that adolescents who reported a parental-set bedtime per se versus those without a set bedtime reported earlier bedtimes, more sleep, and less daytime fatigue experienced. These two studies provide evidence that 1) parental intervention by setting a bedtime results in extended sleep; 2) the combination of earlier bedtime and extended sleep contribute to more positive outcomes for depression, sleepiness, and fatigue. These findings may also indicate that such behavioral interventions as having a fixed, earlier bedtime may help mitigate the circadian phase delay associated with adolescent maturation, likely through preventing light to the circadian timing system at the delay-sensitive evening phase.

Screen time, technology use, and social engagement in the evening become more available as youngsters pass through adolescence. The US National Sleep Foundation poll of adolescents reported that electronic devices (i.e., electronic music devices, television, electronic/video games, cell phone, telephone, computer, Internet access) in the bedroom become more common from grade 6 to grade 12 students: the median number of such devices in bedrooms of younger adolescents was two versus a median of four in the older teens. A recent review by Cain and Gradisar notes that the preponderance of studies report shorter, later, and/or more disrupted sleep, as well as such daytime consequences as sleepiness or disruptive behavior, for children and adolescents as TV watching, computer/Internet/electronic games use, or mobile phone use in the evening before beddime is greater. These activities are arousing in and of themselves and usually more easily accessed by the older adolescents, taking advantage of increased accessibility of technology and of the changes to the sleep regulatory systems that make it easier to stay away later. Indeed, to the extent that the activities involve light exposure-perhaps particularly blue-spectrum light exposure to which the circadian clock may have greater sensitivity-evening light has the phase-specific effect of delaying circadian rhythms, thus pushing sleep timing later.

The impact of school schedules of adolescents is most often to restrict sleep by requiring an early rising time. As noted above, however, school schedules can delay sleep further, such as described for Korean adolescents enrolled in evening classes to improve preparation for academic placement tests. Evening homework assignments often carry a similar challenge for adolescents in the US. More attention has been directed toward school starting times because of the tendency for many school systems in the US to start the school day earlier as children get older, thus requiring teens to arise at an early hour relative to their typical bedtimes, circadian phases, and need for sleep.

One study examined sleep, sleepiness, and circadian rhythms in teens for whom the transition from ninth grade to tenth grade involved an advance of the school start time from 0825 to 0720. highlights findings from this study. Actigraphically monitored sleep data across two weeks confirmed that, although the students woke up significantly earlier on school nights in tenth than ninth grade, they did not go to sleep earlier and consequently experienced less sleep on average. The general level of sleepiness in these students is shown with the daytime sleep latency data, showing a moderate level of sleepiness in ninth grade, and a significant decline in tenth grade to a level considered severely sleepy for the sleep latency at 0830. The 0830 assessment was also associated with the occurrence of REM sleep within 10 or 15 minutes of sleep onset, (a finding often used to aid in the diagnosis of narcolepsy) in about one half of the participants. The study also showed that the time of the dim light melatonin onset (DLMO) was nearly 30 m later on average in the students when assessed in tenth grade; indeed, the DLMO time in the tenth graders was significantly later (2146 vs. 2036) for those with the short sleep latency and REM sleep in the morning nap, interpreted as evidence that the circadian timing system of these teens was phase delayed relative to the early start time and favored sleep during the morning hours they were scheduled to attend school. The association of a circadian phase delay, excessive morning sleep tendency, and REM sleep in morning trials for one-half of the adolescents studied is remarkable and raises the level of concern for early morning classes.

Table 3

The Perfect Storm: Sleep Behavior and Troubling Outcomes

illustrates the confluence of factors that combine for adolescents in the 21 st century to reduce time spent sleeping on school nights below a healthful amount, with waking and expected school performance timed to occur at an inappropriate circadian phase. As reviewed above, bioregulatory and psychosocial forces collude to push sleep onset later, yet schools are timed to begin earlier across adolescence, and sleep time is compressed as a consequence. The list of negative outcomes associated with insufficient sleep is lengthy and ranges from sleepiness and mood disturbances noted above, inattention, poor grades, behavior problems, substance use, driving crashes, overweight, and immune system compromise. For some adolescents, the issue can present as sleep-onset insomnia that may be associated with the circadian phase delay; when combined with lack of motivation, depressed mood, and fatigue, a depressive disorder is often the initial assumption. An approach that targets sleep timing and phase adjustment is likely to be ameliorative.

At the end of the day, the pediatrician can have positive impact by intervening in any of a number of levels:

encourage your loacl school districts to consider starting the school day later for adolescents;

recommend that the local school district also limit late-evening activities;

galvanize schools to provide instructional information about sleep and circadian rhythms;

support and encourage parents to identify and set an appropriate bedtime;

encourage teens to avoid light and stimulating activities in the evening and to get light exposure in the morning;

empower adolescents to make informed choices about their sleep schedules;

remind families of the utility of a relaxing pre-sleep ritual.

Footnotes

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References

# data[].attributes.attachment_link.description
The perfect storm metaphor applies to sleep patterns of adolescents in the sense that developmental trajectories of biopsychosocial factors conspire to limit the quantity of sleep for many adolescents resulting in a number of negative consequences.

# data[].attributes.headline
Sleep Disturbance and Subsequent Suicidal Behaviors in Preadolescence

# data[].attributes.subject
Sleep Disturbance and Subsequent Suicidal Behaviors in Preadolescence

# data[].attributes.attachment_link.title
Sleep Disturbance and Subsequent Suicidal Behaviors in Preadolescence

# data[].attributes.attachment_link.description
This cohort study examines parent- and child-reported types and severity of sleep disruptions and their association with suicidal ideation and suicide attempts in late childhood to early adolescence.

# data[].attributes.headline
Sleep and Athletic Performance: The Impact of Sleep on Strength, Speed, and Endurance

# data[].attributes.subject
Sleep and Athletic Performance: The Impact of Sleep on Strength, Speed, and Endurance

# data[].attributes.body
A detailed look into how sleep or the lack thereof affects various aspects of athletic performance.

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Sports Medicine Clinics of North America

# data[].attributes.attachment_link.description
Sleep is an essential component of health and well-being, with significant impacts on physical development, emotional regulation, cognitive performance, and quality of life. Along with being an integral part of the recovery and adaptive process between bouts of exercise, accumulating evidence sugges …

# data[].attributes.body
Explores how naps can help improve memory and learning among students.

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American Academy of Sleep Medicine - Association for Sleep Clinicians and Researchers

# data[].attributes.attachment_link.readable_text
WESTCHESTER, Ill. - A brief bout of non-REM sleep (45 minutes) obtained during a daytime nap clearly benefits a person's declarative memory performance, according to a study published in the February 1 issue of the journal SLEEP.

The study, authored by Matthew A. Tucker, PhD, of the Center for Sleep and Cognition and the department of psychiatry at Harvard Medical School, focused on 33 subjects (11 males, 22 females) with an average age of 23.3 years. The participants arrived at the sleep lab at 11:30 a.m., were trained on each of the declarative memory tasks at 12:15 p.m., and at 1 p.m., 16 subjects took a nap while 17 remained awake in the lab. After the nap period, all subjects remained in the lab until the retest at 4 p.m.

It was discovered that, across three very different declarative memory tasks, a nap benefited performance compared to comparable periods of wakefulness, but only for those subjects that strongly acquired the tasks during the training session.

"These results suggest that there is a threshold acquisition level that has to be obtained for sleep to optimally process the memory," said Dr. Tucker. "The importance of this finding is that sleep may not indiscriminately process all information we acquire during wakefulness, only the information we learn well."

It is recommended that adults get between seven and eight hours of nightly sleep.

The American Academy of Sleep Medicine (AASM) offers the following tips on how to get a good night's sleep:

Follow a consistent bedtime routine.

Establish a relaxing setting at bedtime.

Get a full night's sleep every night.

Avoid foods or drinks that contain caffeine, as well as any medicine that has a stimulant, prior to bedtime.

Do not bring your worries to bed with you.

Do not go to bed hungry, but don't eat a big meal before bedtime either.

Avoid any rigorous exercise within six hours of your bedtime.

Make your bedroom quiet, dark and a little bit cool.

Get up at the same time every morning.

Those who suspect that they might be suffering from a sleep disorder are encouraged to consult with their primary care physician or a sleep specialist.

SLEEP is the official journal of the Associated Professional Sleep Societies, LLC, a joint venture of the AASM and the Sleep Research Society.

SleepEducation.com, a patient education Web site created by the AASM, provides information about various sleep disorders, the forms of treatment available, recent news on the topic of sleep, sleep studies that have been conducted and a listing of sleep facilities.

For a copy of this article, entitled, "Enhancement of Declarative Memory Performance Following a Daytime Nap is Contingent on Strength of Initial Task Acquisition," or to arrange an interview with an AASM spokesperson regarding this study, please contact Jim Arcuri, public relations coordinator, at (708) 492-0930, ext. 9317, or jarcuri@aasm.org.

# data[].attributes.attachment_link.title
Journal SLEEP: Study Concludes a Daytime Nap Can Benefit a Person's Memory Performance - American Academy of Sleep Medicine - Association for Sleep Clinicians and Researchers

# data[].attributes.attachment_link.description
A brief bout of non-REM sleep (45 minutes) obtained during a daytime nap clearly benefits a person's declarative memory performance, according to a study published in the February 1 issue of the journal SLEEP.

# data[].attributes.headline
Sleep as a Human Right: Understanding the Global Perspective

# data[].attributes.subject
Sleep as a Human Right: Understanding the Global Perspective

# data[].attributes.body
An article discussing sleep as a fundamental human right and how neglecting it affects basic human dignity.

# data[].attributes.attachment_link.description
Recognition of sleep as a human rights issue by governmental and legal entities (as illustrated by recent legal cases in the United States and India) raises the profile of sleep health as a societal concern. Although this recognition may not lead to immediate public policy changes, it infuses the public discourse about the importance of sleep health with loftier ideals about what it means to be human. Such recognition also elevates the work of sleep researchers and practitioners from serving the altruistic purpose of improving human health at the individual and population levels to serving the higher altruistic purpose of promoting human rights for all people. These conditions create an environment in which policy change that values individual and population sleep health can occur gradually over time, particularly in societies with strong democratic traditions.

# data[].attributes.headline
The Right to Sleep: Addressing Sleep Deprivation in Society

# data[].attributes.subject
The Right to Sleep: Addressing Sleep Deprivation in Society

# data[].attributes.body
Examines societal factors leading to sleep deprivation and international efforts to uphold sleep as a right.

# data[].attributes.attachment_link.title
The Global Problem of Insufficient Sleep and Its Serious Public Health Implications

# data[].attributes.attachment_link.description
Good sleep is necessary for good physical and mental health and a good quality of life. Insufficient sleep is a pervasive and prominent problem in the modern 24-h society. A considerable body of evidence suggests that insufficient sleep causes hosts of ...

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Nightshift Work and the Risk of Ovarian Cancer...

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Nightshift Work and the Risk of Ovarian Cancer...

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Abstract

Objectives

Animal evidence suggests that circadian disruption may be associated with ovarian cancer, though very little epidemiologic work has been done to assess this potential association. We evaluated the association between self-reported nightshift work, a known circadian disruptor, and ovarian cancer in a population-based case-control study.

Methods

The study included 1,101 women with invasive epithelial ovarian cancer, 389 women with borderline epithelial ovarian tumors and 1,832 controls and was conducted in Western Washington State. Shift work data was collected as part of in-person interviews.

Results

Working the nightshift was associated with an increased risk of invasive (OR=1.24, 95% CI: 1.04-1.49) and borderline (OR=1.48, 95% CI: 1.15-1.90) tumors; however, we observed little evidence that risks increased with increasing cumulative duration of nightshift work, and risks were not elevated in the highest duration category (>7 nightshift work-years). Increased risks were restricted to women who were 50 years of age and older and to serous and mucinous histologies of invasive and borderline tumors. There was suggestive evidence of a decreased risk of ovarian cancer among women reporting a preference for activity during evenings rather than mornings.

Conclusion

We found evidence suggesting an association between shift work and ovarian cancer. This observation should be followed up in future studies incorporating detailed assessments of diurnal preference (i.e. chronotype) in addition to detailed data on shift schedules.

Keywords: shift work, ovarian cancer, circadian, chronotype

Introduction

It is estimated that over 22,000 new cases of ovarian cancer will be diagnosed in the United States in 2012, with over 15,000 deaths from the disease [ 1]. There are few known factors that influence risk of ovarian cancer, including hormonal contraceptive use, parity and heavier body weight [ 2]. Given the high mortality rate among patients with ovarian cancer, the identification of potentially modifiable risk factors is crucial to disease prevention efforts.

Shift work resulting in circadian disruption has been classified as a probable human carcinogen by the International Agency for Research on Cancer (IARC) [ 3]. Though the mechanisms behind the carcinogenicity of shift work are not fully understood, they are thought to be mediated by the indoleamine melatonin, which is produced at nighttime and suppressed by ambient light [ 4]. Melatonin is a regulator of the hypothalamic-pituitary-gonadal axis, and the effect of melatonin on reproductive hormones, including estrogen, has been considered a primary mechanism by which shift work may be associated with breast cancer risk [ 5].

This mechanism may also extend to ovarian cancer for which there is evidence that endogenous reproductive hormones are involved in the pathogenesis of the disease [ 6]. Evidence suggests that melatonin may be directly involved in ovarian function, including follicular development, ovulation, oocyte maturation and luteal function [ 7]. Extensive animal and cellular experimental data have shown melatonin to have direct oncostatic properties which may be mediated through an effect on reactive oxygen species [ 8]. In fact, in a study of rats, oral administration of melatonin was shown to decrease lipid hydroperoxide levels, increase total levels of antioxidant substances and increase the activity of antioxidant enzymes, including superoxide dismutase, catalase and glutathione-reductase in ovarian tissue [ 9].

Despite this suggestive experimental evidence, few epidemiologic studies have examined the association between shift work and ovarian cancer. Here we describe a large population-based case-control study in which we examined the relation of lifetime nightshift work with risk of epithelial ovarian tumors.

Materials and Methods

Study population

Female residents of a thirteen-county area of western Washington State who were diagnosed with a primary invasive or borderline epithelial ovarian tumor from 2002 through 2009 were considered eligible as cases. From 2002-2005, women aged 35-74 years were included, while from 2006-2009 only women aged 35-69 were included. The cases were identified through a population-based cancer registry, the Cancer Surveillance System (CSS), which is part of the Surveillance, Epidemiology, and End Results (SEER) Program of the US National Cancer Institute. The methods for case ascertainment and for case and control recruitment for 2002-2005 have been previously described [ 10]; similar methods were used for 2006-2009 [ 11]. After excluding 61 cases because of a potential language barrier, 2,025 eligible cases were identified, and 1,502 (74.2%) were interviewed. Of the interviewed cases, 1,108 had invasive disease and 394 had borderline tumors. Histologic type was collected and coded by the CSS using the International Classification of Diseases for Oncology (ICD-O) [ 12] morphology codes and grouped according to the guidelines of the WHO [ 13] as serous, mucinous, endometrioid, clear cell and other epithelial tumors (comprised largely of unspecified adenocarcinoma and carcinoma). Tumor grade was also collected by the CSS, as grades 1 (well-differentiated) through 4 (undifferentiated) or unknown. In addition to assessing risk of invasive and borderline tumors separately, we created the following additional analytic groupings of invasive cancers, based on a recent conceptualization of ovarian cancer pathogenesis [ 14, 15]: high-grade serous (HGSC; comprised of serous morphology, grades 2-4 or unknown, as well as grade 3 or higher tumors that were morphologically classified as endometrioid; n=840), low-grade serous (LGSC; grade 1 serous; n=23); mucinous (MC; n=32); endometrioid (EC; morphologically endometrioid and grade 1, 2 or unknown; n=129); and clear cell tumors (CCC; n=84). Based on molecular similarities observed between 1) borderline serous tumors and invasive LGSC, and 2) borderline and invasive mucinous tumors, we further combined invasive and borderline tumors of these subtypes. Other borderline tumors (n=23) were excluded from the subtype analyses.

Controls were selected by random-digit-dialing (RDD) using stratified sampling in 5-year age categories, 1-year calendar intervals and two county strata. From 2002-2005, a 2:1 ratio of controls to women with invasive ovarian cancer was selected using the Waksberg-Mitofsky RDD method [ 16]. From 2006-2009, a 1:1 ratio of controls to women with invasive disease was used; list-assisted RDD [ 17] was employed during 2006-2007 and Waksberg-Mitofsky methods during 2008-2009. In total, for 19,092 (78.2%) of the 24,400 telephone numbers belonging to residences, we determined whether an eligible woman (i.e., an age and county eligible woman able to communicate in English and, if so, with at least one ovary and no prior history of ovarian cancer) resided there. Of the 2,351 eligible women identified, 1,849 were interviewed (78.6%). The study was approved by the Institutional Review Board of the Fred Hutchinson Cancer Research Center, and all women provided signed informed consent before participating.

Data collection

In-person interviews pertained to the period of time before diagnosis (for cases) or before an assigned comparable reference date (for controls), and covered the following: demographic and lifestyle characteristics; employment history; medical history; and detailed reproductive history, including menstrual, pregnancy, and use of contraceptive and menopausal hormone preparations.

In addition to start and end dates and average hours per week for each job held at least a continuous four months starting at age 25 until the reference date, participants were asked to report how many of their work days over the duration of the job included the hours between midnight and 4 a.m. (nightshift), to which they could respond: never worked those hours; less than half the days; at least half the days; or every work day. As a measure of chronotype (preference for being active in the morning or the evening), which may be associated with the ability to adapt to nightshift work, participants were asked to report if they were morning persons, evening persons or other (assessed as of 5 years before the reference date).

Shift work variables

For the primary analysis, two shift work variables were created: ever/never worked any nightshift and cumulative nightshift work-years (from age 25 until the reference date). For each participant with j jobs, cumulative nightshift work-years ( CN) was calculated as follows:

Where Dj is the total number of months engaged in a particular job, Hj is the hours worked per week for that job and Fj is the fraction of time spent doing nightshift work for that job. For each job, Fj was assigned as 0 if the subject reported that she never worked nightshifts, 0.25 if she reported that less than half of the days involved nightshifts, 0.5 if she reported that at least half of the days involved nightshifts or 1.0 if she reported that every work day involved nightshifts. Work years were calculated by dividing the total number of hours engaged in nightshift work for a particular job by 2080, which is the total number of hours worked in a year assuming an average of 40 hours of work per week.

We also examined ever/never worked in a job with less than half of work days doing the nightshift and ever/never worked a job with all nightshifts. These categorizations were not mutually exclusive, i.e. a participant reporting working one or more jobs with less than half of works days doing the nightshift and one or more jobs with all nightshifts would be in the "ever" exposed category in both analyses.

Statistical analysis

Unconditional logistic regression was used to calculate odds ratios (ORs) and corresponding 95% confidence intervals (CI). All analyses were adjusted for the frequency-matching variables of age at reference (5-year intervals), county of residence (dichotomized as the three urban or 10 rural/suburban counties in the study) and calendar year of diagnosis/reference date (continuous). We also adjusted for factors known to be associated with ovarian cancer: duration of hormonal contraceptive use (never, <6, 6-59, 60-119 or ≥ 120 months), number of full term pregnancies (0, 1, 2 or ≥ 3) and body mass index (BMI) at age 30 (<18.5, 18.5-24.9, 25-29.9 or ≥ 30). Adjustment for additional factors, including race/ethnicity, history of hormone replacement therapy, smoking status, alcohol use and education had a minimal impact (i.e. < 10% change) on point estimates of interest, so these variables were not included in the final analyses. Cumulative nightshift work-years were categorized according to approximate quartiles among controls who were ever engaged in nightshift work. Median number of work-years was also compared between cases and controls using the Wilcoxon rank-sum test given the highly skewed distribution of the variable.

Additional separate analyses were conducted to examine the association of shift work with risk of ovarian cancer among women diagnosed before and after age 50 years and to examine the association of shift work with tumor subtypes (as described above). We also evaluated whether chronotype (morning or evening preference) modified the association between shift work and ovarian cancer. Participants who did not indicate a morning or evening preference were excluded from this analysis. Effect modification was formally evaluated using likelihood ratio tests comparing models with and without chronotype and nightshift work cross-products terms. All analyses were carried out using the STATA version 11 statistical package (Statacorp LP, College Station, Texas).

Results

After excluding 29 subjects with missing covariate data on BMI, number of births or duration of hormonal contraception, a total of 1,101 invasive cancer cases, 389 borderline tumor cases and 1,832 controls were included in our analyses. The distribution of various characteristics of cases and controls is provided in Table 1 . Compared to controls, a lesser proportion of invasive cases used hormonal contraceptives, and invasive and borderline cases tended to have had fewer children than controls. As expected, women with borderline tumors tended to be younger than women with invasive disease.

Table 1

Controls (%)(n=1832)

Invasive Cases (%)(n=1101)

Borderline Cases (%)(n=389)

Age at reference

Race/ethnicity

Duration of hormonal contraceptive use

No. of full term pregnancies

BMI at age 30

Highest attained education

Total number of jobs worked over lifetime

Chronotype

Among the 27% of invasive cases that reported ever working a night job ( Table 2 ), the median number of work-years engaged in the nightshift was 3.5, and among the 32% of borderline cases, the median number of work-years engaged in the nightshift was 3.2. Among the 23% of controls working night jobs, the median number of work-years engaged in the nightshift was 2.7; these differences between the case groups and controls were not statistically significant (p >0.5). Among controls and invasive tumor cases, the majority of jobs that were reported as involving nightshift work were in health care (27% and 28%, respectively), mostly nursing, followed by food preparation and service (e.g. cooks, waitresses, bartenders) (18% and 16%) and office and administrative support (e.g. telephone operators, customer service representatives, dispatchers etc.) (17% and 15%). Among borderline cases, the majority of nightshift jobs were in food preparation and service (24%), followed by health care (20%) and office and administrative support (13%).

Table 2

Invasive

Borderline

Controls (%)(n=1832)

Cases (%)(n=1101)

ORa (95% CI)

Cases (%)(n=389)

ORa (95% CI)

Worked nightshift

Worked nightshift for less than half of all work days

Worked nightshift for all work days

Cumulative nightshift work-years

Ever working the nightshift was associated with a 1.24-fold increased risk of invasive epithelial ovarian cancer (95% CI: 1.04-1.49) and a 1.48-fold increased risk of borderline epithelial ovarian tumors (95% CI: 1.15-1.90). When examining cumulative nightshift work-years, there were no indications of a trend with either invasive or borderline tumors; significant increases in risk were restricted to the second highest cumulative category (>3-7 work-years).

In additional analyses, risks of invasive and borderline tumors among women who reported ever working a job with less than half of the work days as nights (OR invasive=1.28, 95% CI: 1.03-1.59; OR borderline=1.32, 95% CI: 0.98-1.78) and who reported working a job with all nights (OR invasive=1.16, 95% CI: 0.87-1.55; OR borderline=1.29, 95% CI: 0.87-1.92) were not materially different from those observed in the main analyses evaluating any history of ever working the nightshift ( Table 2 ). A greater percentage of evening type individuals had ever engaged in nightshift work than had morning types (27% versus 20% of controls; χ ², p=0.002); while the risks of invasive and borderline ovarian tumors for those ever working the nightshift among self-classified morning type individuals (OR invasive=1.29, 95% CI: 1.00-1.67; OR borderline=1.57, 95% CI: 1.08-2.27) was slightly greater than those ever working the nightshift among self-classified evening type individuals (OR invasive=1.14, 95% CI: 0.85-1.53; OR borderline=1.43, 95% CI: 0.97-2.09), the differential effects were not statistically significant (p invasive=0.47; p borderline=0.54).

When stratifying by reference age, ever working the nightshift was significantly associated with increased risks of both invasive and borderline tumors among women who were 50 years of age or older ( Table 3 ), but risks were not significantly elevated in women under 50 years of age. These apparent differential affects by age were not statistically significant (p invasive=0.32; p borderline=0.65). Table 4 presents the results of the subtype-specific analyses of invasive and borderline disease. Increased risks of HGSC and LGSC (the latter including borderline and invasive disease) were associated with ever working the nightshift (ORs=1.29, 95% CI: 1.06-1.57, and 1.51, 95% CI: 1.12-2.05, respectively). Risk of MC (including invasive and borderline disease) was also positively associated with ever working the nightshift (OR=1.55, 95% CI: 1.10-2.17). In contrast, overall risks of both EC and CCC were, if anything, slightly reduced in association with ever working the nightshift (OR=0.91 for each of these subtypes). To assess whether subtype-specific differences were simply reflecting an age effect or vice versa, we conducted age-specific analyses within histologic subgroups. No clear differences in risk by age within the EC, CCC, MC and LGSC subgroups were observed, while risks among the most common subtype, HGSC, were primarily increased among women aged 50 or more years (data not shown).

Table 3

<50 years of age at reference date

≥50 years of age at reference date

Invasive

Borderline

Invasive

Borderline

Controls (%)(n=444)

Cases (%)(n=274)

ORa (95% CI)

Cases (%)(n=172)

ORa (95% CI)

Controls (%)(n=1388)

Cases (%)(n=827)

ORa (95% CI)

Cases (%)(n=217)

ORa (95% CI)

Worked nightshift

Cumulative nightshift work-years

Table 4

High Grade Serous

Low Grade and Borderline Serousb

Invasive and Borderline Mucinousc

Endometrioid

Clear Cell

Controls (%)(n=1832)

Cases (%)(n=834)

ORa (95% CI)

Cases (%)(n=240)

ORa (95% CI)

Cases (%)(n=181)

ORa (95% CI)

Cases (%)(n=129)

ORa (95% CI)

Cases (%)(n=83)

ORa (95% CI)

Worked nightshift

Cumulative nightshift work-years

Discussion

In our large population-based case-control study, we found evidence suggesting an association between ever working the nightshift and increased risk of epithelial ovarian tumors. The increased risks that we observed are consistent in magnitude with those observed for breast cancer in previous studies [ 18]. However, we did not observe a trend with increasing cumulative duration of nightshift work, nor did we observe an increased risk in the highest category of cumulative duration. There was suggestive evidence that increased risks were restricted to women 50 years of age and older. In tumor subtype analyses, elevated risks associated with nightshift work were observed for HGSC, LGSC and MC subtypes (the latter two including both invasive and borderline tumors), but not with EC or CCC. While risks associated with most reproductive factors have been consistent across all histologic types [ 19], associations with certain factors such as smoking, endometriosis and BMI have been observed to vary by histology [ 19- 21].

Chronotype has been considered by others as a measure of adaptability to shift work schedules, with evening type individuals reporting better tolerance to nightshift work (e.g. better work performance and higher job satisfaction) than morning type individuals [ 22]. In the current study, a greater proportion of evening type individuals among our controls had a history of nightshift work, but there was only suggestive evidence that ovarian cancer was elevated to a lesser extent among such individuals than among morning type persons.

In a recent prospective analysis of 718 ovarian cancer cases among 181,548 participants in the Nurse's Health Study, there was no evidence of an association between rotating shift work and ovarian cancer risk [ 23]. Rotating shift work was defined as working at least 3 nights per month in addition to day or evening shifts and has been previously associated with breast, endometrial and colorectal cancers in the cohort [ 24- 26]. The study did not collect data on permanent/fixed nightshift work. Though our study did not specifically ascertain data on rotating shift work, it is reasonable to assume a rotating shift work schedule for those reporting working jobs with less than half of the work days involving the nightshift. We found evidence of increased risks of invasive ovarian cancer in association with nightshift work even when restricting analyses to this group. It is difficult to directly compare the results of our study to those of the Nurse's Health Study given the differences in shift work data that were collected.

Animal and laboratory data suggest that an increased risk of ovarian cancer associated with circadian disruption may be plausible. In turkey breeder hens with ovarian adenocarcinomas, longer day periods (i.e. longer exposure to light) promoted growth of ovarian tumors, while shorter day periods were found to inhibit growth [ 27]. Direct administration of melatonin was also found to inhibit tumor growth. As has been hypothesized for breast cancer, melatonin may impact ovarian cancer risk through an effect on endogenous levels of reproductive hormones, particularly estrogen. Though evidence suggests that estrogens may be especially important in the etiology of EC and CCC [ 6], we did not observe associations between nightshift work and risk of these subtypes. Beyond its effects on reproductive hormones, melatonin has been shown to have direct oncostatic properties. In addition to directly scavenging reactive oxygen and nitrogen species, melatonin stimulates other antioxidants and enzymes that metabolize reactive species [ 7]. In a study of female rats, ethanol was found to increase lipid hydroperoxide levels in ovarian tissue; with concurrent melatonin administration, lipid hydroperoxide levels were significantly decreased, and the antioxidant activities of superoxide dismutase, glutathione peroxidase and glutathione reductase were all increased [ 28].

The large size of our study and comprehensive data on historic nightshift schedules, as well as important potential confounders, are strengths. Though participation rates were relatively high for both cases and controls, participation bias may have influenced study results if those women that chose not to participate in our study differed from study subjects in factors related to nightshift work. Although data on nightshift work was retrospectively collected through self-report, the potential association of shift work with ovarian cancer was not a primary hypothesis of the study that was expressed to participants, so recall bias is unlikely. Further, while study participants may have been aware of light-at-night as a hypothesized risk factor for other cancers, little or no attention had been paid to ovarian cancer during the years in which our study was conducted. The prevalence of shift work in our study population is higher than what has been reported for the general US working population by the Bureau of Labor Statistics (BLS) (17.7% among US salaried workers) [ 29]. However, the higher prevalence is consistent with the types of jobs that study subjects reported working. For example, among controls, the most commonly worked jobs involving the nightshift were in the health care industry, and according to the BLS, over 24% of salaried health care workers in the US were engaged in shift work in 2004. Food preparation and service was the next most common class of job involving nightshift work that controls reported. According to BLS data, over 49% of salaried US food preparation and service workers were engaged in shift work in 2004.

We did not observe a significant dose-response relationship with cumulative nightshift work-years, which may be attributable to exposure misclassification. For instance, the fraction of time spent working the nightshift for a particular job could, in actuality, be any value between 0 and 1, but for this study, we were only able to assign one of four values to this fraction. Also, information on intensity of nightshift schedules was not ascertained as part of this study, meaning that a participant working a full-time job with 2 nights per week over a period of 10 years would be treated the same in our analyses as a participant working the nightshift for the first 2 years of a full-time job that lasted for 10 years. In addition, the exposure variables, while relatively detailed, may not have captured the aspect of exposure most linked with risk. For example, with rotating shift work schedules, the rate of rotation and direction of rotation may be particularly important [ 30], but that information was not collected as part of this study. We did, however, have data on chronotype, which may be a critical modifier of the association between nightshift work and cancer but mostly has not been evaluated in studies nightshift work and cancer risk. However, this variable was based on a response to a single question, and there are much more comprehensive assessment tools available such as the Circadian Type Inventory (18-item questionnaire), which assign scores to classify individuals on a morningness-eveningness scale [ 31]. Chance must also be considered as an explanation of our findings.

We found suggestive evidence that nightshift work is associated with increased risks of both invasive and borderline ovarian tumors, particularly of serous and mucinous subtypes and among women who are 50 years of age and older. However, our results contrast with those of the single prior study of ovarian cancer risk that evaluated rotating shift work. Further research is needed, and future studies should incorporate assessments of chronotype to further explore whether evening-type persons are at a decreased risk of developing ovarian cancer as a result of better adaptability to shift work schedules. These studies should also evaluate the impact of genetic variation in circadian genes in conjunction with chronotype on susceptibility to the potential carcinogenic effects of nightshift work. Prospectively designed studies are likely to provide the best option for collecting the detailed high-quality shift work data that are needed. However, for rare diseases such as ovarian cancer, individual cohorts may lack power to detect significant associations, particularly with respects to stratification on chronotype, genetic variation or tumor morphology.

​

Acknowledgments

Funding
The study was funded by grants R01 CA112523 and R01 CA87538 from the US National Cancer Institute.

Footnotes

Competing Interests
None to report.
Licence Statement
The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence (or non-exclusive for government employees) on a worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if accepted) to be published in Occupational and Environmental Medicine and any other BMJPGL products to exploit all subsidiary rights, as set out in our licence ( http://group.bmj.com/products/journals/instructions-for-authors/licence-forms) and the Corresponding Author accepts and understands that any supply made under these terms is made by BMJPGL to the Corresponding Author.

References

1. American Cancer Society, Cancer Facts and Figures 2012. Atlanta (GA): American Cancer Society; 2012. [ Google Scholar]

2. Hankinson SE, Danforth KN. Cancer Epidemiology and Prevention. New York: Oxford University Press; 2006. Ovarian Cancer; pp. 1013-26. [ Google Scholar]

11. Bodelon C, Cushing-Haugen KL, Wicklund KG, et al. Sun exposure and risk of epithelial ovarian cancer. Cancer Causes Control. doi: 10.1007/s10552-012-0076-x. Published Online First: 12 October 2012. [[ PMC free article] [ PubMed] [ CrossRef] Google Scholar]

12. Fritz A. International classification of diseases for oncology ICD-O. 3. Geneva: WHO Press; 2000. [ Google Scholar]

13. WHO Classification of Tumors. Pathology and genetics of tumours of the breast and female genital organs. Lyon, France: IARC Press; 2003. [ Google Scholar]

20. Pearce CL, Templeman C, Rossing MA, et al. Association between endometriosis and risk of histological subtypes of ovarian cancer: a pooled analysis of case-control studies. The Lancet Oncology. 2012;13:385-94. [[ PMC free article] [ PubMed] Google Scholar]

21. Collaborative Group on Epidemiological Studies of Ovarian Cancer. Ovarian Cancer and Body Size: Individual Participant Meta-Analysis Including 25,157 Women with Ovarian Cancer from 47 Epidemiological Studies. PLoS Med. 2012;9:e1001200. [[ PMC free article] [ PubMed] Google Scholar]

23. Poole EM, Schernhammer ES, Tworoger SS. Rotating night shift work and risk of ovarian cancer. Cancer Epidemiol Biomarkers Prev. 2011;20:934-8. [[ PMC free article] [ PubMed] Google Scholar]

# data[].attributes.attachment_link.title
Nightshift work and risk of ovarian cancer

# data[].attributes.attachment_link.description
Animal evidence suggests that circadian disruption may be associated with ovarian cancer, though very little epidemiologic work has been done to assess this potential association. We evaluated the association between self-reported nightshift work, a known ...

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Mental Health Conditions According to Stress and Sleep Disorders

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The purpose of this study was to compare associations between stress and sleep disorders (insomnia, hypersomnia, and sleep apnea), identify potential modifying effects, and compare associations between stress and types of sleep disorders with selected mental health conditions. Analyses were based on 21,027 employees aged 18–64 years in 2020 who were insured by the Deseret Mutual Benefit Administrators (DMBA). The risk of stress (2.3%) was significantly greater in women, singles, and those with dependent children. The risk of a sleep disorder was 12.1% (2.1% for insomnia, 1.0% for hypersomnia, and 10.1% for sleep apnea). The risk of stress was significantly greater for those with a sleep disorder (136% overall, 179% for insomnia, and 102% for sleep apnea after adjusting for age, sex, marital status, dependent children, and sleep disorders). The risk of stress among those with sleep apnea was significantly greater for singles than for married individuals. Approximately 9.5% had anxiety, 8.5% had depression, 2.0% had ADHD, 0.6% had bipolar disorder, 0.4% had OCD, and 0.1% had schizophrenia. Each of these mental health conditions was significantly positively associated with stress and sleep disorders. Bipolar disorder and schizophrenia were more strongly associated with stress and sleep disorders than were the other mental health conditions. Insomnia was more strongly associated with anxiety, bipolar disorder, OCD, and schizophrenia than was sleep apnea.

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Sleep quality, duration, and consistency are associated with better academic performance in college students

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Abstract

Although numerous survey studies have reported connections between sleep and cognitive function, there remains a lack of quantitative data using objective measures to directly assess the association between sleep and academic performance. In this study, wearable activity trackers were distributed to 100 students in an introductory college chemistry class (88 of whom completed the study), allowing for multiple sleep measures to be correlated with in-class performance on quizzes and midterm examinations. Overall, better quality, longer duration, and greater consistency of sleep correlated with better grades. However, there was no relation between sleep measures on the single night before a test and test performance; instead, sleep duration and quality for the month and the week before a test correlated with better grades. Sleep measures accounted for nearly 25% of the variance in academic performance. These findings provide quantitative, objective evidence that better quality, longer duration, and greater consistency of sleep are strongly associated with better academic performance in college. Gender differences are discussed.

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Introduction

The relationship between sleep and cognitive function has been a topic of interest for over a century. Well-controlled sleep studies conducted with healthy adults have shown that better sleep is associated with a myriad of superior cognitive functions,1,2,3,4,5,6 including better learning and memory.7,8 These effects have been found to extend beyond the laboratory setting such that self-reported sleep measures from students in the comfort of their own homes have also been found to be associated with academic performance.9,10,11,12,13

Sleep is thought to play a crucial and specific role in memory consolidation. Although the exact mechanisms behind the relationship between sleep, memory, and neuro-plasticity are yet unknown, the general understanding is that specific synaptic connections that were active during awake-periods are strengthened during sleep, allowing for the consolidation of memory, and synaptic connections that were inactive are weakened.5,14,15 Thus, sleep provides an essential function for memory consolidation (allowing us to remember what has been studied), which in turn is critical for successful academic performance.

Beyond the effects of sleep on memory consolidation, lack of sleep has been linked to poor attention and cognition. Well-controlled sleep deprivation studies have shown that lack of sleep not only increases fatigue and sleepiness but also worsens cognitive performance.2,3,16,17 In fact, the cognitive performance of an individual who has been awake for 17 h is equivalent to that exhibited by one who has a blood alcohol concentration of 0.05%.1 Outside of a laboratory setting, studies examining sleep in the comfort of peoples' own homes via self-report surveys have found that persistently poor sleepers experience significantly more daytime difficulties in regards to fatigue, sleepiness, and poor cognition compared with persistently good sleepers.18

Generally, sleep is associated with academic performance in school. Sleep deficit has been associated with lack of concentration and attention during class.19 While a few studies report null effects,20,21 most studies looking at the effects of sleep quality and duration on academic performance have linked longer and better-quality sleep with better academic performance such as school grades and study effort.4,6,9,10,11,12,13,22,23,24,25,26,27 Similarly, sleep inconsistency plays a part in academic performance. Sleep inconsistency (sometimes called "social jet lag") is defined by inconsistency in sleep schedule and/or duration from day to day. It is typically seen in the form of sleep debt during weekdays followed by oversleep on weekends. Sleep inconsistency tends to be greatest in adolescents and young adults who stay up late but are constrained by strict morning schedules. Adolescents who experience greater sleep inconsistency perform worse in school.28,29,30,31

Although numerous studies have investigated the relationship between sleep and students' academic performance, these studies utilized subjective measures of sleep duration and/or quality, typically in the form of self-report surveys; very few to date have used objective measures to quantify sleep duration and quality in students. One exception is a pair of linked studies that examined short-term benefits of sleep on academic performance in college. Students were incentivized with offers of extra credit if they averaged eight or more hours of sleep during final exams week in a psychology class32 or five days leading up to the completion of a graphics studio final assignment.33 Students who averaged eight or more hours of sleep, as measured by a wearable activity tracker, performed significantly better on their final psychology exams than students who chose not to participate or who slept less than eight hours. In contrast, for the graphics studio final assignments no difference was found in performance between students who averaged eight or more hours of sleep and those who did not get as much sleep, although sleep consistency in that case was found to be a factor.

Our aim in this study was to explore how sleep affects university students' academic performance by objectively and ecologically tracking their sleep throughout an entire semester using Fitbit-a wearable activity tracker. Fitbit uses a combination of the wearer's movement and heart-rate patterns to estimate the duration and quality of sleep. For instance, to determine sleep duration, the device measures the time in which the wearer has not moved, in combination with signature sleep movements such as rolling over. To determine sleep quality, the Fitbit device measures the wearer's heart-rate variability which fluctuates during transitions between different stages of sleep. Although the specific algorithms that calculate these values are proprietary to Fitbit, they have been found to accurately estimate sleep duration and quality in normal adult sleepers without the use of research-grade sleep staging equipment.34 By collecting quantitative sleep data over the course of the semester on nearly 100 students, we aimed to relate objective measures of sleep duration, quality, and consistency to academic performance from test to test and overall in the context of a real, large university college course.

A secondary aim was to understand gender differences in sleep and academic performance. Women outperform men in collegiate academic performance in most subjects35,36,37,38 and even in online college courses.39 Most of the research conducted to understand this female advantage in school grades has examined gender differences in self-discipline,40,41,42 and none to date have considered gender differences in sleep as a mediating factor on school grades. There are inconsistencies in the literature on gender differences in sleep in young adults. While some studies report that females get more quantity43 but worse quality sleep compared with males,43,44 other studies report that females get better quality sleep.45,46 In the current study, we aim to see whether we would observe a female advantage in grades and clarify how sleep contributes to gender differences.

Results

Bedtime and wake-up times

On average, students went to bed at 1:54 a.m. (Median = 1:47 a.m., Standard Deviation (SD) of all bedtime samples = 2 h 11 min, SD of mean bedtime per participant = 1 h) and woke up at 9:17 a.m. (Median = 9:12 a.m., SD of all wake-up time samples = 2 h 2 min; SD of mean wake-up time per participant = 54 min). The data were confirmed to have Gaussian distribution using the Shapiro-Wilks normality test. We conducted an ANOVA with the overall score (sum of all grade-relevant quizzes and exams-see "Procedure") as the dependent variable and bedtime (before or after median) and wake-up time (before or after median) as the independent variables. We found a main effect of bedtime ( F (1, 82) = 6.45, p = 0.01), such that participants who went to bed before median bedtime had significantly higher overall score ( X = 77.25%, SD = 13.71%) compared with participants who went to bed after median bedtime ( X = 70.68%, SD = 11.01%). We also found a main effect of wake-up time ( F (1, 82) = 6.43, p = 0.01), such that participants who woke up before median wake-up time had significantly higher overall score ( X = 78.28%, SD = 9.33%) compared with participants who woke up after median wake-up time ( X = 69.63%, SD = 14.38%), but found no interaction between bedtime and wake-up time ( F (1, 82) = 0.66, p  = 0.42).

A Pearson's product-moment correlation between average bedtime and overall score revealed a significant and negative correlation ( r (86) = −0.45, p < 0.0001), such that earlier average bedtime was associated with a higher overall score. There was a significant and negative correlation between average wake-up time and overall score ( r (86) = −0.35, p < 0.001), such that earlier average wake-up time was associated with a higher overall score. There was also a significant and positive correlation between average bedtime and average wake-up time (r (86) = 0.68, p  < 0.0001), such that students who went to bed earlier tended to also wake up earlier.

Sleep duration, quality, and consistency in relation to academic performance

Overall, the mean duration of sleep for participants throughout the entire semester was 7 h 8 min (SD of all sleep samples = 1 h 48 min, SD of mean sleep duration per participant = 41 min). There was a significant positive correlation between mean sleep duration throughout the semester (sleep duration) and overall score ( r (86) = 0.38, p < 0.0005), indicating that a greater amount of sleep was associated with a higher overall score (Fig. 1a). Similarly, there was a significant positive correlation between mean sleep quality throughout the semester (Sleep Quality) and Overall Score ( r (86) = 0.44, p < 0.00005). Sleep inconsistency was defined for each participant as the standard deviation of the participant's daily sleep duration in minutes so that a larger standard deviation indicated greater sleep inconsistency. There was a significant negative correlation between sleep inconsistency and overall score ( r (86) = −0.36, p < 0.001), indicating that the greater inconsistency in sleep duration was associated with a lower overall score (Fig. 1b).

Correlations between sleep measures and overall score. a Average daily hours slept (sleep duration) vs. overall score for the semester. b Standard deviation of average daily hours of sleep (sleep inconsistency) vs. overall score in class

Timing of sleep and its relation to academic performance

To understand sleep and its potential role in memory consolidation, we examined the timing of sleep as it related to specific assessments. All Pearson correlations with three or more comparisons were corrected for multiple comparisons using false discovery rate.47

Night before assessments

We conducted a correlation between sleep quality the night before a midterm and respective midterm scores as well as sleep duration the night before a midterm and respective scores. There were no significant correlations with sleep duration or sleep quality for all three midterms (all rs < 0.20, all ps > 0.05). Similar analyses for sleep duration and sleep quality the night before respective quizzes revealed no correlations ( rs from 0.01 to 0.26, all p s > 0.05).

Week and month leading up to assessments

To understand the effect of sleep across the time period while course content was learned for an assessment, we examined average sleep measures during the 1 month leading up to the midterms. We found a significant positive correlation between average sleep duration over the month leading up to scores on each midterm ( rs from 0.25 to 0.34, all ps < 0.02). Similar analyses for average sleep duration over one week leading up to respective quizzes were largely consistent with those of midterms, with significant correlations on 3 of 8 quizzes (rs from 0.3 to 0.4, all ps < 0.05) and marginal correlations on an additional 3 quizzes (rs from 0.25 to 0.27, all p s < 0.08).

There was a significant and positive correlation between sleep quality scores averaged over the month leading up to each midterm for all three midterms ( rs from 0.21 to 0.38, all ps < 0.05). Similar analyses for average Sleep Quality over one week leading up to respective quizzes revealed a significant correlation on 1 of 8 quizzes ( r (86) = 0.42, p < 0.005) and marginal correlations on 3 quizzes ( rs from 0.25 to 0.27, all p s < 0.08).

Variance of assessment performance accounted for by sleep measures

In order to calculate how much of the variance on assessment performance was accounted for by the sleep measures, we conducted a stepwise regression on overall score using three regressors: sleep duration, sleep quality, and sleep inconsistency. The relative importance of each variable was calculated using the relaimpo package in R 48 to understand individual regressor's contribution to the model, which is not always clear from the breakdown of model R ² when regressors are correlated. We found a significant regression ( F (3,84) = 8.95, p = .00003), with an R ² of 0.24. Students' predicted overall score was equal to 77.48 + 0.21 (sleep duration) + 19.59 (Sleep Quality) - 0.45 (sleep inconsistency). While sleep inconsistency was the only significant individual predictor of overall score ( p  = 0.03) in this analysis, we found that 24.44% of variance was explained by the three regressors. The relative importance of these metrics were 7.16% sleep duration, 9.68% sleep quality, and 7.6% sleep inconsistency.

Gender differences

Females had better Sleep Quality ( t (88) = 2.63, p = 0.01), and less sleep inconsistency ( t (88) = 2.18, p = 0.03) throughout the semester compared with males, but the two groups experienced no significant difference in sleep duration ( t (88) = 1.03, p = 0.3). Sleep duration and sleep quality were significantly correlated in both males ( r (41) = 0.85, p < 0.00001) and females ( r (43) = 0.64, p < 0.00001), but this correlation was stronger in males ( Z = −2.25, p = 0.02) suggesting that it may be more important for males to get a long-duration sleep in order to get good quality sleep. In addition, sleep inconsistency and sleep quality were significantly negatively correlated in males ( r (41) = −0.51, p = 0.0005) but not in females ( r (43) = 0.29, p  > 0.05), suggesting that it may be more important for males to stick to a regular daily sleep schedule in order to get good quality sleep.

Females scored higher on overall score compared with males ( t (88) = −2.48, p = 0.01), but a one-way analysis of covariance (ANCOVA) revealed that females and males did not perform significantly different on overall score when controlling for Sleep Quality, F (1, 85) = 2.22, p = 0.14. Sleep inconsistency and overall score were negatively correlated in males ( r (41) = −0.44, p = 0.003) but not in females ( r (43) = −0.13, p  = 0.39), suggesting that it is important for males to stick to a regular sleep schedule in order to perform well in academic performance but less so for females. No other gender differences were detected between other sleep measures and overall score.

Discussion

This study found that longer sleep duration, better sleep quality, and greater sleep consistency were associated with better academic performance. A multiple linear regression revealed that these three sleep measures accounted for 24.44% of the variance in overall grade performance. Thus, there was a substantial association between sleep and academic performance. The present results correlating overall sleep quality and duration with academic performance are well aligned with previous studies6,11,12,24,25 on the role of sleep on cognitive performance. Similarly, this study compliments the two linked studies that found longer sleep duration during the week before final exams47 and consistent sleep duration five days prior to a final assignment48 enhanced students' performance. The present study, however, significantly extends our understanding of the relation between sleep and academic performance by use of multiple objective measures of sleep throughout an entire semester and academic assessments completed along the way.

The present study also provides new insights about the timing of the relation between sleep and academic performance. Unlike a prior study,23 we did not find that sleep duration the night before an exam was associated with better test performance. Instead we found that both longer sleep duration and better sleep quality over the full month before a midterm were more associated with better test performance. Rather than the night before a quiz or exam, it may be more important to sleep well for the duration of the time when the topics tested were taught. The implications of these findings are that, at least in the context of an academic assessment, the role of sleep is crucial during the time the content itself is learned, and simply getting good sleep the night before may not be as helpful. The outcome that better "content-relevant sleep" leads to improved performance is supported by previous controlled studies on the role of sleep in memory consolidation.14,15

Consistent with some previous research45,46 female students tended to experience better quality sleep and with more consistency than male students. In addition, we found that males required a longer and more regular daily sleep schedule in order to get good quality sleep. This female advantage in academic performance was eliminated once sleep patterns were statistically equated, suggesting that it may be especially important to encourage better sleep habits in male students (although such habits may be helpful for all students).

Several limitations of the present study may be noted. First, the sleep quality measures were made with proprietary algorithms. There is an evidence that the use of cardiac, respiratory, and movement information from Fitbit devices can accurately estimate sleep stages,32 but there is no published evidence that Fitbit's 1~10 sleep quality scores represent a valid assessment of sleep quality. Second, the relation between sleep and academic performance may be moderated by factors that can affect sleep, such as stress, anxiety, motivation, personality traits, and gender roles. Establishing a causal relation between sleep and academic performance will require experimental manipulations in randomized controlled trials, but these will be challenging to conduct in the context of real education in which students care about their grades. Third, these findings occurred for a particular student population at MIT enrolled in a particular course, and future studies will need to examine the generalizability of these findings to other types of student populations and other kinds of classes.

In sum, this study provides evidence for a strong relation between sleep and academic performance using a quantifiable and objective measures of sleep quality, duration, and consistency in the ecological context of a live classroom. Sleep quality, duration, and consistency together accounted for a substantial amount (about a quarter) of the overall variance in academic performance.

Methods

Participants

One hundred volunteers (47 females) were selected from a subset of students who volunteered among 370 students enrolled in Introduction to Solid State Chemistry at the Massachusetts Institute of Technology to participate in the study. Participants were informed of the study and gave written consent obtained in accordance with the guidelines of and approved by the MIT Committee on the Use of Humans as Experimental Subjects. Due to limitations in funding, we only had access to 100 Fitbit devices and could not enroll all students who volunteered; consequently, the first 100 participants to volunteer were selected. All participants were gifted a wearable activity tracker at the completion of the study in exchange for their participation. Seven participants were excluded from analysis because they failed to wear their activity tracker for more than 80% of the semester, three participants were excluded because they lost their wearable activity tracker, and another two participants were excluded because they completed less than 75% of the assessments in the class. Of the 88 participants who completed the study (45 females), 85 were freshmen, one was a junior and two were seniors (mean age = 18.19 years).

The Solid State Chemistry class is a single-semester class offered in the fall semester and geared toward freshmen students to satisfy MIT's general chemistry requirement. The class consisted of weekly lectures by the professor and two weekly recitations led by 12 different teaching assistants (TAs). Each student was assigned to a specific recitation section that fit their schedule and was not allowed to attend other sections; therefore, each student had the same TA throughout the semester. Students took (1) weekly quizzes that tested knowledge on the content covered the week leading up to the quiz date, (2) three midterms that tested knowledge on the content covered in the 3-4 weeks leading up to the exam date, and (3) a final exam that tested content covered throughout the semester. Based on a one-way between subjects' analysis of variance (ANOVA) to compare the effect of teaching assistants (TAs) on overall grade, we found no significant differences in overall grade across the TAs (F (10, 77) = 1.82, p  = 0.07. (One TA was removed from the analysis because he only had one student who was participating in this study).

Procedure

Participants were asked to wear an activity tracker for the entire duration of the semester without going below 80% usage each week. If 80% or more usage was not maintained, warning emails were sent at the end of that respective week. Participants were asked to return the device if they dipped below 80% usage more than three out of the 14 weeks of the semester. The average usage rate at the end of the semester for the 88 participants who completed the study was 89.4% (SD = 5.5%). The missing data appeared to be at random and were deleted prior to data analysis. As part of a separate research question, 22 of the 88 participants joined an intense cardiovascular exercise class for which they received separate physical education credit. These students performed similarly to the other 67 participants in terms of final class grade ( t (88) = 1.57, p = 0.12), exercise amount (total amount of moderately and very active minutes on the wearable device) (t (88) = 0.59, p = 0.56), sleep amount ( t (88) = 0.3, p = 0.77), and sleep quality ( t (88) = 0.14, p  = 0.9), so they were included in all of the analyses.

Materials

Participants' activities were tracked using a Fitbit Charge HR. Data from the device were recorded as follows: heart rate every 5 min; steps taken, distance traveled, floors climbed, calories burned and activity level measurements every 15 min; resting heart rate daily; and sleep duration and quality for every instance of sleep throughout the day. Sleep quality was determined using Fitbit's proprietary algorithm that produces a value from 0 (poor quality) to 10 (good quality).

Assessments

Nine quizzes, three midterm examinations, and one final examination were administered throughout the 14-week class to assess the students' academic achievement. The students' cumulative class grade was made up of 25% for all nine quizzes (lowest quiz grade was dropped from the average), 15% for each midterm exam, and 30% for the final exam for a total of 100%.

At MIT, freshmen are graded on a Pass or No Record basis in all classes taken during their first semester. Therefore, all freshmen in this class needed a C- level or better (≥50%, no grading on a curve) to pass the class. A failing grade (a D or F grade) did not go on their academic record. All upperclassmen were given letter grades; A (≥85%), B (70-84%), C (50-69%), D (45-49%), F (≤44%). Because a large portion of the class had already effectively "passed" the class before taking Quiz 9 and the final exam, we excluded these two assessments from our analyses due to concerns about students' motivation to perform their best. We calculated for each student an overall score defined as the sum of the eight quizzes and three midterms to summarize academic performance in the course.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

References

1. Clark, M. J. & Grandy, J. Sex differences in the academic performance of scholastic aptitude test takers. ETS Res. Rep. Ser. 2, 1-27 (1984).

Google Scholar

2. Willingham, W. W. & Cole, N. S. Gender and fair assessment. (Mahwah, NJ, US, Lawrence Erlbaum Associates, 1997).

3. Volchok, E. Differences in the performance of male and female students in partially online courses at a community college. Community Coll. J. Res. Pract. 1-17, https://doi.org/10.1080/10668926.2018.1556134 (2018).

4. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57, 289-300 (1995).

Google Scholar

5. Grömping, U. Relative importance for linear regression in R: The package relaimpo. J. Stat. Softw. 17, 1-27 (2015).

Google Scholar

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Acknowledgements

This research was supported by a grant from the Horace A. Lubin Fund in the MIT Department of Materials Science and Engineering to J.C.G. and funding from MIT Integrated Learning Initiative to K.O. and J.R.K. The authors are grateful for many useful discussions with Carrie Moore and Matthew Breen at the Department of Athletics, Physical Education, and Recreation at MIT.

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Authors and Affiliations

1. MIT Integrated Learning Initiative, Department of Brain and Cognitive Sciences, and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

Kana Okano, Jakub R. Kaczmarzyk & John D. E. Gabrieli

2. Harvard Business School, Boston, MA, 02163, USA

Neha Dave

3. Department of Materials Science and Engineering Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

Jeffrey C. Grossman

Contributions

K.O. and J.C.G. conceived, designed, supervised, and analyzed the project. J.K. and N.D. helped analyze the data. The manuscript was written by K.O., J.D.E.G., and J.C.G.

Corresponding author

Correspondence to Jeffrey C. Grossman.

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Okano, K., Kaczmarzyk, J.R., Dave, N. et al. Sleep quality, duration, and consistency are associated with better academic performance in college students. npj Sci. Learn. 4, 16 (2019). https://doi.org/10.1038/s41539-019-0055-z
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Sleep quality, duration, and consistency are associated with better academic performance in college students - npj Science of Learning

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Although numerous survey studies have reported connections between sleep and cognitive function, there remains a lack of quantitative data using objective measures to directly assess the association between sleep and academic performance.

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A 4-year longitudinal study investigating the relationship between flexible school starts and grades

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A 4-year longitudinal study investigating the relationship between flexible school starts and grades

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Abstract

The mismatch between teenagers' late sleep phase and early school start times results in acute and chronic sleep reductions. This is not only harmful for learning but may reduce career prospects and widen social inequalities. Delaying school start times has been shown to improve sleep at least short-term but whether this translates to better achievement is unresolved. Here, we studied whether 0.5-1.5 years of exposure to a flexible school start system, with the daily choice of an 8 AM or 8:50 AM-start, allowed secondary school students (n = 63-157, 14-21 years) to improve their quarterly school grades in a 4-year longitudinal pre-post design. We investigated whether sleep, changes in sleep or frequency of later starts predicted grade improvements. Mixed model regressions with 5111-16,724 official grades as outcomes did not indicate grade improvements in the flexible system per se or with observed sleep variables nor their changes-the covariates academic quarter, discipline and grade level had a greater effect in our sample. Importantly, our finding that intermittent sleep benefits did not translate into detectable grade changes does not preclude improvements in learning and cognition in our sample. However, it highlights that grades are likely suboptimal to evaluate timetabling interventions despite their importance for future success.

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Introduction

During adolescence, teenagers undergo a plethora of biological and socially-driven developments that also influence their sleep-wake behaviour1,2,3,4. Their internal phase (chronotype) delays progressively with age until around 215, while sleep pressure (the homeostatic load) likely accumulates more slowly across the day compared to adults6,7. This predisposes teenagers, more than younger children or adults, to delay into late evening hours thereby also delaying their sleep timing. Early school start times cut teenagers' sleep artificially short in the morning, forcing them to get up before they reach healthy amounts of 8-10 h of night-time sleep on schooldays. On weekends, teenagers sleep not only longer but also later which better suits their delayed circadian clock. This sleep timing difference between school and free days is called "social jetlag", since it is a constant jetlag situation induced by social schedules8,9. These widespread sleep restrictions are not only connected to compromised health and well-being10,11,12,13,14, but also reduced cognition (e.g. constructive15 and creative thinking skills16,17 or verbal fluency18,19) and decreased academic performance20.

There is now substantial amount of evidence that delayed school start times help to increase sleep durations towards more healthy amounts, at least in the short-term21,22,23,24. However, it is less clear whether the increased sleep durations and better sleep quality also translate to better learning in the same students. This could reasonably be expected, given the prominent role of sleep for memory, sustained attention and concentration e.g.25,26,27.

Thus, several studies previously tried to evaluate the effects of delayed school start times on grades or scores. These studies yielded very mixed results, probably due to differences in study designs, interventions, exposure times and outcome measures28. However, all interventions assessed were static changes in school start times whereas the possibility to make school start times flexible has been largely overlooked although previously raised in a commentary29.

Here, we studied whether a flexible school start system, as implemented in a secondary school in Germany30,31, and concurrent changes in sleep were associated with changes in grades in multiple academic disciplines. The flexible system entailed that the school changed from a permanent fixed start at mainly 8 AM to a flexible school start that allowed senior students to choose daily whether to attend school at 8 AM or skip the first class and start at 8:50 AM. The flexible system was associated with a variety of short- and long-term changes in students' sleep patterns on which we report in detail elsewhere30,31. In summary, on days with later starts, students slept on average about 1 h longer by delaying their offset times by 1 h but maintaining the same average onset times. Subjective sleep quality was improved, and the amount of alarm-driven waking slightly reduced. This was seen both immediately after changing into the flexible system30 as well as after 1 full year in the system31. Nonetheless, sleep patterns did not improve from the conventional to the flexible system overall since, on average, students did not accumulate enough late starts during the time of monitoring to result in a significant difference of the weekly average. However, there was wide variation between students on the number of late starts chosen and the sleep gain or loss achieved30,31.

In the present study, we analysed students' quarterly grades from 12 academic school subjects across 4 years to examine effects of this new system on academic grades in detail. With 2.5 years of data prior and up to 1.5 years after the introduction of the flexible school start, we could control for some important confounders and address trends and complex interactions which started long before the system was changed. In addition, we also used longitudinal sleep data (duration, chronotype, social jetlag), which we had previously collected in these students30,31 to predict students' quarterly grades by means of linear mixed regression models.

Methods and materials

German secondary school system

The German school system varies widely between federal states, but all students start formal schooling with primary school (children aged ~ 6-9) followed by several possible types of mostly public secondary schools. The minimum mandatory schooling years are 9-10 years. The current study took place at a public "Gymnasium", which is the most academic secondary school type in Germany, granting access to higher education at the university level after successful completion of the final exam ("Abiturprüfung"). Gymnasiums offer a very broad education and thus specialisations are not allowed until senior years (grades 11-13) when students can then decide to focus on specific subjects. Core subjects (e.g. maths, German and one main foreign language) cannot be deselected and-depending on the state-are examined in the final exam. About 34% of all secondary school students in Germany attend Gymnasium32.

Study site and the flexible school start system

The study took place at the Gymnasium Alsdorf (50° 53′ N, 6° 10′ E), a public secondary school in the West of Germany. This particular school offers daily self-study periods during which students work through a personal 5-week curriculum with a teacher and on a subject of their own choice (so-called "Dalton system"33,34) to foster independent learning. This is different to most other Gymnasiums in Germany, which work with mostly teacher-centred methods and no personal curriculum.

On February 1st, 2016, the school changed permanently from a fixed start ("conventional system") to a flexible start ("flexible system") for 10-12th graders (senior students). In the conventional system, school started at 8 AM on most days. On a median of 1 day/week (depending on students' individual timetables), however, school started with the second period at 8:50 AM. The conventional system is a common system seen across different Gymnasiums in Germany, although the exact start time might vary slightly between schools (~ 7:30-8 AM). The amount of afternoon classes and the closing times vary depending on students' grade level and the school's individual schedule. Most often, students have 2-4 long school days where they attend afternoon classes finishing roughly between 1 and 5 PM.

In the flexible system, the first period (lasting 08:00-08:45 AM) was made optional for senior students to attend. Senior students could thus choose daily whether to start at 8 AM with the first self-study period or skip it and start at 08:50 AM instead (called "9 AM" here for convenience) with a normal teacher-centred class. On a median of 1 day/fortnight, students also had a scheduled free second period (08:50-09:50 AM), i.e. the chance to start at 10:15 AM (" > 9 AM"). Given the low occurrence rate of > 9 AM-starts, we did not distinguish between frequencies of 9 AM-starts and > 9 AM-starts in our analyses. Skipped first periods had to be made up for within the same week, either during gap periods or after classes ended. Skipped first periods had to be made up for within the same week, either during gap periods or after classes ended.

Free first or second periods or gap periods during the day are common in secondary schools in Germany. This reflects the different choices senior students make to focus on specialised subjects which results in individualised schedules for each student. For more information on this unique flexible system, please refer to30 which also includes an example timetable.

Study design

Official academic grades were obtained from the school registry for students that took part in our first wave in 201630 and the second wave in 201731. While grades were provided retrospectively at the end of the schoolyear of wave 2 for the past 4 years, sleep data were collected longitudinally in both waves (Fig. 1): Wave 1 consisted of baseline sleep diary data collection (= t0) covering 3 weeks in January, 2016 (Jan 8th to 31st) in the conventional system, followed by sleep diary data collection for 6 weeks (Feb 1st to Mar 14th) in the flexible system right after its introduction on Feb 1st, 2016 (= t1). Wave 2 covered the matching photoperiod and time of t1, lasting again 6 weeks (Feb 2nd to Mar 20th, 2017, = t2). No second baseline just before t2 was carried out since the school had remained in the flexible system. We excluded any sleep diary entries during the carnival holiday periods between February 4th-9th, 2016 and February 23rd-28th, 2017 from the analyses.

Study design and outcome measures. Schematic of longitudinal study design including quarterly academic grades from up to 2.5 years prior to and up to 1.5 years after the introduction of the flexible system. The same students had also provided daily sleep diary data in two waves (one baseline assessment in the conventional system and 2 time points in the flexible system as described previously31.

Participants

Written informed consent was obtained from all participants (or their parents/guardians if < 18y). The study was conducted according to the Declaration of Helsinki and approved by the school board, the parent-teacher association, the school's student association and the ethics committee of the Medical Faculty of the LMU Munich (#774-16). We used opportunity sampling without specific exclusion criteria. For response and attrition rates and filter criteria of sleep diary data and cohorts please refer to31. Academic grades were provided by the school registry from students that took part at any time during our study (t0 through t2). All included students were granted promotion to the next grade level during the study period.

Outcome measures

Sleep diary

We used a daily sleep diary (provided online via LimeSurvey.org) based on the μMCTQ35. Students provided sleep onset (note: not bedtime) and offset (wake time) of their past night's sleep, the type of day they woke up (schoolday or free day), and when they started school (8 AM, 9 AM or > 9 AM). The questionnaire did not cover any naps during the day. For detailed sleep diary descriptions, please see30,31.

Academic grades

From the school registry, we obtained official quarterly grades awarded to participating students between the school year 2013/2014 through to 2016/2017. Of the 170 students from both waves qualifying for analysis (i.e. the cohorts previously described and used for sleep analyses; see31), 13 students had grades missing, thus resulting in a maximum sample of 157 students for the grade analyses. For the majority of these students (62%), grade data span 2.5 years in the conventional and 1.5 years in the flexible system; for those in grade level 10 at wave 2 (18%), it was 3 and 1 years, and for those at grade level 12 at wave 1 (15%) it was 2.5 and 0.5 years. The grades were provided for all academic subjects taken by a student, of which we included only 12 subjects in our analyses that most students took and assigned them to one of three disciplines: Sciences (Biology, Chemistry, Maths, Physics, Natural Sciences), Social Sciences (Geography, History), and Languages (English, German, Spanish, French, Latin). Provided grades were averages per academic quarter per academic subject over a mixture of written and oral examinations, course work and participation in class.

The school year lasted from the end of August to mid-July divided into the following quarters: quarter 1 until end of October, quarter 2 until third week of January, quarter 3 until third week of April and quarter 4 until first week of July.

In grade levels 7-10, the grading scale ranged from 1 (best) to 6 (worst) with grades ≤ 4 considered passing grades. This scale was additionally broken down into plus ( +) and minus (−) for all but grade 6 (1+, 1, 1−, 2+, 2, 2− and so on). In grade levels 11 and 12, the scale ranged from 0 (worst) to 15 (best) with ≥ 4 considered passing. Both scales were combined by transforming the 1-6 scale to a 0-15 scale based on its finer plus/minus system (i.e. 15 = 1+, 14 = 1, 13 = 1−, 12 = 2+, 11 = 2, 10 = 2− and so on) and then transformed to a more universal 0-100% scale (0 = 0%, 15 = 100%, then steps of 6.66% for grades in between).

Data analysis

Analyses were performed in SPSS Statistics (IBM, versions 24 and 25) and R (versions 3.6.1 and 3.6.3) using R studio (versions 1.1.463, 1.2.1335 and 1.2.5042). Graphs were produced using the r-package ggplot2 36.

Sleep data

Daily sleep data from dairies were aggregated and taken from t2 if available (else from t1). From these aggregates, we derived the following variables as per equations below: average daily sleep duration during the week (SD week), chronotype as midsleep on free days (MSF) corrected for oversleep (MSF sc), social jetlag (SJL) as midsleep on free days (MSF) minus midsleep on work/school days (MSW), and frequency of ≥ 9 AM-starts. For the linear mixed Models 3a-d, we additionally calculated the absolute differences between t0 and t1 (i.e., from baseline to the flexible system during wave 1) for SD week, MSF sc and SJL (X change).

$${\text{SD}}_{{{\text{week}}}} = ({\text{SD}}_{{{\text{schooldays}}}}*5 + {\text{ SD}}_{{{\text{free days}}}}*2)/7$$

$${\text{MSF}}_{sc} = {\text{ SleepOnset}}_{{{\text{free days}}}} + \frac{1}{2}{\text{SD}}_{{{\text{week}}}}$$

$${\text{SJL}} = {\text{MSF}} - {\text{MSW}}$$

$${\text{Frequency of}}\;{ } \ge 9{\text{AM-starts}} = { }({\text{n}}_{{9{\text{AM-starts}}_{{{\text{flex}}}} }} /{\text{n}}_{{{\text{schoolday-entries}}_{{{\text{flex}}}} }} )*100$$

$${\text{X}}\;{\text{ change}} = {\text{x}}_{{{\text{t}}1}} - {\text{x}}_{{{\text{t}}0}}$$

Statistical analyses

Unless indicated otherwise, descriptive statistics are reported as mean ± standard deviation and test statistics are abbreviated as follows: t, t-test; Pearson correlation; rho, Spearman rank correlation; b, unstandardized coefficient of linear regression or linear mixed models; b flex*change, unstandardized coefficient of the interaction of linear mixed models; p, significance level. The alpha-level was set to p  < 0.05 for all statistical analyses. All data were tested on normality (histograms, QQ plots, Shapiro-Wilk's test) and sphericity.

For simple grade analyses comparing accumulative grade point averages in the conventional versus the flexible system, a two-sided paired t-test was used. To this end, the grade point average was calculated as the mean grade across all subjects before or after the start-system change for each student. For more sophisticated grade analyses, we used linear mixed-effects regression models ( lme4 and lmer test package37,38 in R). In total, 4 different models (plus model variations) were calculated to answer different questions based on different fixed effects, interaction terms and subcohorts (see overview Table 1). Student ID was added as random effect to all models to incorporate unsystematic differences between individuals. In all models, the outcome (dependent variable) was quarterly grades per discipline per student; the fixed effects (independent variables) were system (conventional/flexible), gender (female/male), grade level (7-12), academic quarter (1-4), and academic discipline (Sciences/Social Sciences/Languages), all entered as categorical variables. Model 1 additionally included interaction terms between discipline and gender to assess general grade influences, Model 2 included interaction terms between school start system and gender, and system and discipline to assess system effects per discipline and gender. In Models 3, we included one of the aggregated sleep-change variables (see equation above; mean-centred) as additional fixed effects, each in interaction with system (conventional/flexible): chronotype change (Model 3a), sleep duration on schooldays change (Model 3b), social jetlag change (Model 3c) or frequency of ≥ 9 AM-starts (Model 3d). In Model 4, we instead included the absolute value of chronotype, sleep duration on schooldays, social jetlag, and frequency of ≥ 9 AM-starts for the flexible system only (from t2 if available, else from t1 to maximize sample size). Since chronotype, sleep duration on schooldays, social jetlag, and frequency of ≥ 9 AM-starts were prone to collinearity, we first assessed their correlations before adding them into the models (Supplementary Fig. S1). Only chronotype and social jetlag were at least moderately correlated (rho = 0.65, p < 0.001; Supplementary Fig. S1), and results from models including just one of these variables each (4a-d) were essentially similar to Model 4e which included all sleep variables together (Supplementary Table S4). The variance inflation factor ( car package in R39) also indicated no problematic collinearity for Model 4e. Marginal means of model estimates were calculated using emmeans in R40 for models where interactions were significant. All linear mixed models were visualised in tables using the sjPlot and sjmisc packages41,42 and in figures as marginal means via the ggeffects package43 in R. Simple contrast results from interactions in linear mixed models were averaged over the levels of system or gender (depending on the model), grade level, and quarter; degrees of freedom method used was Kenward-Rogers. Pairwise comparisons were adjusted with Tukey method.

Table 1 Overview of linear mixed model analyses on official, quarterly grades.

Results

In this study, we investigated whether a flexible school start system and concurrent changes in sleep (as previously described here30,31) were associated with changes in academic grades. During a first wave of sleep assessment30, the studied secondary school had changed from a conventional start system (mostly starting at 8:00 AM; baseline = t0) to a flexible start system with a daily choice between 8:00 and 8:50 AM (= t1; Fig. 1). The school has since maintained this system allowing for a second wave of sleep assessment after exactly one year (t2)31.

For the current study, we included quarterly grades of 63-157 students from these two waves irrespective of time of participation (i.e. t0/t1, t2 or all time points) (Fig. 1). The sample size varies depending on the analysis question and thus with the respective regression model calculated (see Table 1). The majority of included students were females (63-68%), were in grade levels 10 or 11 (but levels 9 and 12 were also included), and used the late-start option (" ≥ 9 AM-use") on about 24-28% of all recorded schooldays (see Table 2 for more cohort characteristics). In total, we analysed 5111-16,724 grades (on average 107 individual grades per person) that students received in 12 academic subjects over 2.5 years in the conventional system and 0.5 to 1.5 years in the flexible system (Fig. 1). Grades were provided by the school registry and transformed to a 0-100% scale and labelled (not aggregated) as Languages, Sciences, or Social Sciences. Median grades were 53-60% in Languages, 60% in Sciences, and 60-67% in Social Sciences (Table 2). To complement the analyses, we also used several of the existing sleep variables (chronotype expressed as MSF sc, sleep duration, social jetlag) in the flexible system and their respective change (delta) from baseline (t0) to the flexible system (t1) as well as the frequency of ≥ 9 AM-use in several model specifications (Tables 1 and 2).

Table 2 Composition of study cohorts. Displayed are cohort characteristics after standard filter criteria.

School start system showed no systematic effect on academic grades overall

First, we investigated whether the flexible system allowed students to increase their grades without considering sleep variables. At first sight, a simple comparison of overall grades yielded a small but statistically significant improvement in grade point average from 58.2% (± 2.1 SD) in the conventional to 59.6% (± 2.0 SD) in the flexible system (Fig. 2a; t[154] = − 2.15, p = 0.033, d z = 0.173). However, attributing this improvement to the flexible system is likely unwarranted. As outlined in the introduction, grades are influenced by a multitude of factors, thus comparisons that do not account for these can be misleading. We therefore applied linear mixed-effects regression models to adjust for potential confounders and including random intercept for ID to account for inter-individual differences (Table 1). When incorporating gender, grade level (i.e. indirectly age), academic quarter, and discipline in addition to school start system in the analysis, the flexible system showed no systematic relationship with students' grades (Fig. 2b; b = − 0.10, p = 0.815, Model 1, Supplementary Table S1), hence the flexible system was not associated with students receiving better or worse grades overall in our sample (n students  = 157).

Longitudinal analysis of official quarterly grades-effects of school start system and general predictors. Quarterly grades (0-100%) from 12 academic subjects of 3 disciplines for 4 years i.e., for most students this was 2.5 years before and 1.5 years after the flexible school start was introduced (n = 157 students; 16,724 grades; 107 grades per student on average). (a) Simple, unadjusted comparison of average grades across all disciplines in the conventional and the flexible school start system via paired t-test (n ID = 157). Shown are mean and 95% CI within the raw data distribution (violin plots). The apparent grade improvement in the flexible system was not confirmed in linear mixed models. ( b-g) Visualization of mixed-model-determined influences on grades. Plots show marginal means from Models 1 and 2 (Supplementary Table S1), i.e. the estimated grade and 95% CI for the reference category (female student, class level 10, quarter 1, languages, conventional system). Statistical significance is indicated in (b), results of more complex cases can be found in the text and Supplementary Tables S1 and S2. ( b) Effect of school start system (Model 1). (c) Effect of grade level (Model 1). ( d) Effect of academic quarter (Model 1). ( e) Effect of academic discipline by gender (Model 1). ( f) Effect of school start system by gender (Model 2). ( g) Effect of school start system by academic discipline (Model 2).

Grades varied systematically with grade level, academic quarter, discipline and gender

But what drives better grades in the unadjusted comparison if not the flexible system itself? The same factors that we adjusted for in the regressions also stood out as major predictors (Model 1, Supplementary Table S1, n students = 157): Students in 12th grade (the last school year) did consistently better compared to their peers across all other grade levels-a sort of "leavers effect" that has already been observed before (Fig. 2c, b = 3.44, p < 0.001)44. Moreover, we found that students enjoyed a bump in grades in the last quarter of the school year with an estimated improvement of 2.3 percentage points compared to the first quarter (Fig. 2d; b = 2.34, p  < 0.001). The combination of these two effects might explain the statistically significant improvement observed in the unadjusted comparison: the flexible system replaced the conventional system mid-year between quarter 2 and 3, so quarter 4 and higher grade levels were overrepresented in the flexible system, which the t-test could not account for.

The mixed models also revealed other strong systematic influences on grades in our sample. Firstly, we observed a clear difference between the disciplines: students performed generally best in Social Sciences, followed by Sciences and then Languages (Model 1, Supplementary Table S1). Post-hoc tests (Fig. 2e) showed that these differences were highly significant for both genders (all p < 0.001; post-hoc to Model 1, Supplementary Table S2), except for girls' grades in Sciences and Social Sciences, which were indistinguishable (b = − 0.47, p = 0.3895; post-hoc to Model 1, Supplementary Table S2).

Female gender has been reported as another driving force for higher grades45. However, girls in our sample did not outperform boys overall (Model 2, Supplementary Tables S1 and S2). Girls were significantly better in Languages (Fig. 2e; b = 4.72, p = 0.0284; post-hoc to Model 1, Supplementary Table S2), while boys surpassed them in the Social Sciences (b = − 3.31, p = 0.1269; post-hoc to Model 1, Supplementary Table S2), and both genders did equally well in Sciences (b = − 0.00, p = 0.9915; post-hoc to Model 1, Supplementary Table S2).

The flexible system was linked with subtle improvements in languages and subtle drops in social sciences grades

Although we did not find evidence that the flexible system was linked with better grades overall (Model 1, see above), the flexible system might be linked with grade improvements in certain disciplines and genders. To assess this, we looked at the interaction between (1) school start system and discipline, as well as (2) school start system and gender in a second model (Model 2; Supplementary Table S1, n students = 157). Neither females nor males significantly improved their overall grades from the conventional to the flexible system (Fig. 2f; post-hoc to Model 2, Supplementary Table S2). In terms of discipline effects, we found that grades in Social Sciences slightly dropped (b = 1.26, p = 0.0384; post-hoc to Model 2, Supplementary Table S2), Science grades remained unchanged (b = − 0.07, p = 0.8849; post-hoc to Model 2, Supplementary Table S2), and Language grades slightly improved (b = − 1.30, p = 0.0168, post-hoc to Model 2, Supplementary Table S2) in the flexible system. Notably, these changes were subtle but reduced the grade differences between the academic disciplines (Fig. 2g, Supplementary Table S2). These small changes in opposite directions likely explain the absence of a net effect of the flexible system on overall grades.

Improvements in chronotype, sleep duration, and social jetlag did not systematically improve grades

What was the role of sleep parameters on grade developments? We speculated that students who showed greater improvements in the flexible system (i.e., advanced chronotype, lengthened sleep duration, and lowered social jetlag) also received better grades in the flexible system. Thus, we computed changes in sleep from t0 (baseline) to t1 for each student in the subpopulation of students with sleep parameters during these time points (n = 63; Table 2). On average, these students showed a small delay in chronotype of 0.2 h (MSF sc, range = − 1.6-2.2 h), a small reduction in social jetlag of 0.3 h (− 2.0-1.9 h) and a small sleep gain of 0.1 h (− 1.3-1.1 h) but large inter-individual differences as reflected by the ranges. Adding these parameters separately into a third model (Models 3a-c, Supplementary Table S3, Fig. 3a), we found that neither changes in chronotype (flex*chronotype change: b = 0.10, p = 0.845, Fig. 3a,b) nor changes in sleep duration (flex*sleep duration change: b = − 0.77, p = 0.352, Fig. 3a,c) were systematically associated with changes in grades. Surprisingly, however, students who increased their social jetlag in the flexible system obtained slightly better grades in the flexible system (flex*social jetlag change: b = 1.28, p = 0.027; Fig. 3 a,d), which was contrary to our hypothesis. Therefore, our analyses in this subsample suggest that sleep improvements experienced immediately after transitioning to the flexible system did not result in detectable higher academic achievement.

Longitudinal analysis of official quarterly grades-effects of sleep and 9 AM-use. Results from linear mixed model analyses of quarterly grades (0-100%) considering sleep variables and the frequency of ≥ 9 AM-starts (see Table 2 for sample descriptions). ( a,f) Schematic of the structure and results from Models 3 and 4 (Supplementary Tables S3 and S4) showing the outcome, official quarterly grades (center), all predictors (black-framed boxes), the statistical significance of their effect (arrows; black: p < 0.05, grey: p ≥ 0.05), the unstandardized regression coefficients (b-values) and ID as random intercept (dashed box). General predictors (white) are categorical variables, so the levels with the highest impact are shown compared to their reference (female, grade level 10, quarter 1, languages). b-values are approximate in ( a), indicated by ≈, as representing results from Models 3a-d. ( a) Effect of changes in sleep and of 9 AM-use on grade improvements from the conventional to the flexible system. Summarized results from Models 3a-d (n ID = 63; Supplementary Table S3) where each model variation included a different yellow predictor in interaction with school start system (conventional/flexible; b flex*change) to model effects of sleep changes on grade changes. ( b-e) Visualization of the yellow interaction effects from ( a) via marginal means, i.e. grade estimates and 95% CI for the reference (female student, class level 10, quarter 1, languages) and categorical splits in the continuous sleep change variables to facilitate display. The effect of school start system on grades by ( b) chronotype change (advance/delay), ( c) sleep duration change (sleep loss/sleep gain), ( d) social jetlag change (reduction/increase) from the conventional to the flexible system, and by ( e) the frequency of 9 AM-use (< 2 days/ ≥ 2 days) in the flexible system. ( f) Effect of absolute sleep characteristics on grades in the flexible system. Summarized results from Model 4e (n ID = 129; Supplementary Table S4) predicting grades only for the flexible system, i.e., 1.5 years post-change, including the red sleep predictors in one common model after running separate Models (4a-d) to check for collinearity. ( g-j) Visualization of the red effects from ( f) via marginal means, i.e. grade estimates and 95% CI for the reference (female student, class level 10, quarter 1, languages). * p < 0.05; ** p < 0.01; *** p  < 0.001.

If not linked to sleep improvements, were grades nonetheless linked with the choice of more later school starts? The results of Model 3d calculated to answer this question suggests that higher 9 AM-use was associated with worse grades in the conventional system (b = − 3.04, p = 0.015), a link reversed partly-albeit not significantly-in the flexible system (flex*9 AM-use: b = 0.59, p = 0.101; Fig. 3a,e and Supplementary Table S3). Hence, students who made high use of the late-start option in the flexible system (group median was 24% of schooldays or 1 day/week; Table 2) were predominantly students obtaining lower grades, but they tended to benefit at least slightly from more later starts.

No systematic effects of chronotype, social jetlag and sleep duration on grades

Lastly, we investigated if we could find absolute effects of sleep variables (chronotype, social jetlag, sleep duration) and ≥ 9 AM-use on grades in the flexible system (Model 4, n = 129 students). Table 2 shows descriptive statistics of these three variables in this cohort (notice the wide range): chronotype or midsleep time on weekends corrected for oversleep was on average at 4:40 AM (range = 2:10-8:35), social jetlag was on average 2.0 h (0.5-6.0 h) and students slept on average 7.2 h (5.2-9.0 h). In contrast to what we had expected, none of the sleep parameters showed any significant link with grades, independent of whether they were added separately into the model (Model 4a-d; Supplementary Table S4) or together (Model 4e; Fig. 3f, Supplementary Table S4). Our results thus indicate that late chronotypes in our sample were not worse off compared to their early peers (Fig. 3f,g) and that longer sleep duration in the flexible system did not improve grades received in the flexible system (Fig. 3f,h). Similarly, social jetlag did not hamper grades to such a degree that we could detect an effect (Fig. 3f,i). Furthermore, frequency of attending school later was at a median of 1.5 days per schoolweek in this sample (28% of schooldays, IQR = 10-52%; Table 2), and we found that per every additional day a student chose to go to school later, grade estimates non-significantly decreased by 1.32 in the full Model 4e ( p = 0.272, Fig. 3f,j; but see the single Model 4d in the Supplementary Table S4: b = − 2.12, p  = 0.022). Although the interpretation is slightly different, this result tallies with the above finding from Model 3: At first sight, it looks as if attending school later more often would prevent students from getting better grades, but we argue that most likely it is the other way around; students who receive worse grades also liked to attend school later more often when they had the chance to do so. Overall, we could not show that chronotype and social jetlag negatively influenced grades, and it seemed as if mainly students who previously achieved lower grades in our sample liked to use the ≥ 9 AM option.

Discussion

Adolescence is a decisive time in life for teenagers around the world. Teenagers undergo many cognitive, emotional and brain structural changes that also shape their risk-taking behaviour, learning capacities and motivation to attend school46,47. A prominent change also occurs in their daily sleep-wake behaviour: teenagers tend to phase-delay their sleep-wake behaviour, which essentially means that they become night-owls5,48,49,50,51,52. This delayed phase, however, clashes with early school starts seen across many countries, thus cutting sleep short in the morning hours during the school week. Apart from many other negative (health) consequences10,11,53,54,55,56, short and low-quality sleep as well as sleepiness likely influences academic success20,57, which in turn is an important determinant of future career trajectories58. Since sleep restrictions and poor sleep habits are more severe in minority groups and disadvantaged students59,60, addressing this problem is key to closing the achievement gap between social groups. However, the evidence is not conclusive whether delayed school start times can ameliorate this pressing health and performance problem. Many previous studies suffer from study design limitations, outcome variables are not comparable, and long-term studies that track individuals over time are rare28. Here, we studied whether a novel timetabling system-a daily chosen flexible school start-has the potential to improve academic grades via improved sleep.

In our study, we found that the flexible system was only associated with higher grades at first sight. When not adjusting for confounding factors, we observed a small improvement of grades in the flexible system, which would be in line with some previous studies e.g.,55,56. However, we argue that such simple pre-post analysis of aggregated grades is not suited to answer this complex question-although this has frequently been done using cross-sectional data. Studies on grades that performed proficient analyses, such as mixed regression models44, quantile regression models61 or difference-in-difference approaches62,65,64 accounting for available confounders provided mixed results and mostly small effect sizes (for a systematic review see28). Nonetheless, positive effects of delayed school start times on academic achievement have been widely proclaimed-bound to raise falsely high expectations in parents and teachers. When we considered grade level, discipline and quarter in mixed model analyses, we found that the flexible system was clearly not associated with overall grade improvements except for subtle increases in Languages and subtle decreases in Social Sciences. In fact, the "confounders" weighed much stronger in our sample than any school start system effect on individual disciplines: graduating students did constantly better, highest grades were given in the final quarter of the year, and students were most successful in Social Sciences. Furthermore, the interplay between gender, discipline and school start system on grades is complex.

Importantly, we also did not find any expected relationships between chronotype, social jetlag, or sleep duration with grades in our sample. Neither changes in these sleep parameters from the conventional to the flexible system nor their absolute values in the flexible system showed any link with grades-except for changes in social jetlag. Surprisingly, an increase in social jetlag, not a decrease, in the flexible system was predictive of higher grades in the flexible system. We have not been able to identify obvious explanations for this finding in exploratory analyses, except for the fact that weekend sleep was much more variant and backed by fewer data points than schoolday sleep, pointing towards a potential chance finding. A likely explanation for our null-finding for the other sleep parameters is a possible lack of power in our sample of 157 students (even though we have > 16,000 longitudinal grades) given the small effect sizes previously identified (ranging around < 0.1 SD; see28). A second possibility is that the time frame students were exposed to the new system was too short (exposure length) or that the delay was too little or infrequent (dose) in our study. Furthermore, sleep variables obtained at discrete study points might not be reflective of sleep during the other academic quarters or years. Thus, we cannot preclude that we missed a subtle effect in our sample but any such effect is likely extremely small. This is also in line with several other studies that were unable to find any effect or meaningful improvements28.

Importantly, the fact that we did not detect systematic improvements in students' grades does not mean that there were no improvements in learning. There is a substantial body of evidence supporting that both acute and chronic sleep loss compromises alertness, cognitive performance and memory, and reduces engagement to perform well (performance effort)15,65,66. Thus, improving sleep in sleep-deprived teenagers is very likely to improve their learning67,70,69. In addition, one could speculate that the flexibility and the thus putatively increased self-responsibility and self-determination of students in the flexible system, paired with the reported increase in motivation on later days, may also further improve learning. The question is whether better learning mediated by improved sleep also translates into better grades-and how much sleep improvement is needed and within what timeframe.

Additionally, students' learning is strongly affected by many factors beyond those captured in our study or those of others on this topic. Models of teaching and learning include several nested factors, such as the individual student (e.g. motivation and prior knowledge), the individual teacher (professional competence70,71), the learning environment (e.g. socio-economic status or native language) or factors of instruction (generic and subject-specific instructional quality) but also class-level factors (learning atmosphere or class mates)72. Of these, especially instruction and teacher-level factors greatly influence students' learning73,74. Furthermore, grades are inherently suboptimal measures of students' academic performance, as teachers also include other factors such as compliance, effort, attitude, or behaviour in their assessment75.

Therefore, it may be a big ask and possibly naive to expect grades to improve noticeably and within a few months after delays or a flexible system have been affected. Rather, we should acknowledge students' maintained achievements under potentially less effort and improved learning capacities (this needs to be assessed in future studies) in addition to the gift of more sleep and better well-being. Indeed, teachers at the studied school reported perceiving students as more alert and motivated and tardiness rates as decreased (personal communication). On the other hand, grades still determine future career trajectories and open doors to higher education in many countries76. In this sense, they do have a greater importance for careers-at least early on-than other measures of performance, such as standardised or non-standardised tests in class, which might be more valid for measuring academic performance under certain conditions. Additionally, despite all described influences, grades-as indicators of prior knowledge-seem to be the best predictor for achievement in university courses77.

Our study has several limitations that have not yet been mentioned. The analyses are based on quarterly aggregates of grades, so we could not consider the type of grades (oral, written, etc.) and their weights thus prohibiting detailed analyses on grade types and their possible competence aspects (e.g. knowledge, attitude, ability).

Additionally, we did not obtain information about teachers' competence, their instructional quality or classroom atmosphere but accounted for gender, quarter, grade level, and discipline-factors that are often overlooked in the field. We also lacked socio-demographic information, which likely influence grades, such as the socioeconomic status (SES), parents' education or ethnicity of students and their parents78. The vast majority of students included in this study were Caucasian by observation, so we had low variation with regards to ethnicity. Furthermore, the school studied was a Gymnasium, which is the most academic secondary school type in Germany leading directly to higher education. Therefore the generalisability of our results to non-Gymnasium secondary students in Germany(~ 65%32) is limited. Lastly, we did not collect objective measures of cognitive performance through cognitive test batteries but asked students to self-evaluate their quality of study and concentration levels31. We also did not collect information on napping behaviour, which might have led to an underestimation of sleep duration overall thus underestimating the positive effects of flexible start times on sleep.

In conclusion, we highlight that current early school start times around the globe are detrimental for sleep and health and likely do not allow students to excel as much as they could. Many studies have shown positive effects on sleep or well-being, when school starts were delayed21,22,23,24 or in systems were school already starts much later, such as in Uruguay or Argentina79,80. Thus, it seems fair to argue that later starts are beneficial for students in terms of health and well-being. These factors form a profound basis for good academic achievement but there are also numerous other factors that play into this and possibly mask positive effects: for example, teachers might not perform at their best later during the day or adjust their grading under the new bell times to achieve normal distributions of performance; the dose of delay might need to be higher (i.e. more delay or more uptake of later starts in a flexible system) and exposure time might need to be longer until an effect emerges, or grades are insensitive to this kind of intervention, to name only a few. But despite these complications, it should be emphasized that students can maintain their grades in addition to better sleep and well-being-a central and very important achievement in its own right. Indeed, students might need to spend less time on their homework, learn more easily or show improved sustained attention while still achieving similar but not improved grades. This question should be addressed in future research, e.g. by distributing questionnaires on time use data in general but also time spent on homework, learning and other academic activities, or running cognitive and attention tests in the field (such as the psychomotor vigilance task assessed for example with the NASA PVT + app81). Furthermore, more studies are required on how to harness the unique advantages of flexible start systems, such as promoting students' responsibility, choice and investment, for optimal sleep and learning gains.

References

1. Mullington, J. M., Haack, M., Toth, M., Serrador, J. M. & Meier-Ewert, H. K. Cardiovascular, inflammatory, and metabolic consequences of sleep deprivation. Prog. Cardiovasc. Dis. 51, 294-302 (2009).

CAS PubMed PubMed Central Google Scholar

2. Raniti, M. B. et al. Sleep duration and sleep quality: Associations with depressive symptoms across adolescence. Behav. Sleep Med. https://doi.org/10.1080/15402002.2015.1120198 (2017).

3. Biller, A. M., Meissner, K., Winnebeck, E. C. & Zerbini, G. School start times and academic achievement: A systematic review on grades and test scores. Sleep Med. Rev. https://doi.org/10.1016/j.smrv.2021.101582 (2021).

4. Biller, A. M., Molenda, C., Zerbini, G., Roenneberg, T. & Winnebeck, E. C. One year later: Longitudinal effects of flexible school start times on teenage sleep and subjective psychological outcomes. Scientific Reports (2021). https://doi.org/10.1038/s41598-022-06209-4.

5. Statistisches Bundesamt. Schulen auf einen Blick. 1-56 https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bildung-Forschung-Kultur/Schulen/Publikationen/Downloads-Schulen/broschuere-schulen-blick-0110018169004.pdf?__blob=publicationFile (2016).

6. Parkhurst, H. Education on the Dalton Plan (E.P. Dutton & Company, 1922).

Google Scholar

7. Dalton International. https://daltoninternational.org/

8. Wickham, H. et al. R package ggplot2: Elegant Graphics for Data Analysis. Version 3.2.1. https://cran.r-project.org/package=ggplot2 (2019).

9. Bates, D., Mächler, M., Bolker, B. & Walker, S. R package: Fitting Linear Mixed-Effects Models Using lme4. Version 1.1-8. (2015).

10. Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. R package lmerTest: Test in Linear Mixed Effects Models. Version 3.0-1. (2018).

11. Fox, J. et al. R package car: Companion to Applied Regression. Version 3.0-8. (2020).

12. Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. R package emmeans: Estimated Marginal Means, aka Least-Square Means. Version 1.4.6. (2020).

13. Lüdecke, D., Bartel, A., Schwemmer, C., Powell, C. & Djalovski, A. R package sjPlot: Data Visualization for Statistics in Social Science. Version 2.8.4. (2020).

14. Lüdecke, D., Giné-Vásquez, I. & Bartel, A. R package sjmisc: Data and Variable Transformation Functions. Version 2.8.4. (2020).

15. Lüdecke, D. & Aust, F. R package ggeffects: Create Tidy Data Frames of Marginal Effects for 'ggplot' from Model Outputs. Version 0.15.0. (2020).

16. Dahl, R. E. & Lewin, D. S. Pathways to adolescent health: Sleep regulation and behavior. J. Adolesc. Heal. https://doi.org/10.1016/S1054-139X(02)00506-2 (2002).

Article Google Scholar

17. Crowley, S. J., Wolfson, A. R., Tarokh, L. & Carskadon, M. A. An update on adolescent sleep: New evidence informing the perfect storm model. J. Adolesc. https://doi.org/10.1016/j.adolescence.2018.06.001 (2018).

Article PubMed PubMed Central Google Scholar

18. Crowley, S. J., Cain, S. W., Burns, A. C., Acebo, C. & Carskadon, M. A. Increased sensitivity of the circadian system to light in early/mid-puberty. J. Clin. Endocrinol. Metab. 100, 4067-4073 (2015).

CAS PubMed PubMed Central Google Scholar

19. Carskadon, M. A. Evolution of sleep and daytime sleepiness in adolescents. Sleep/wake Disord. Nat. Hist. Epidemiol. Long-Term Evol. (1983).

20. Hagenauer, M. H., Perryman, J. I., Lee, T. M. & Carskadon, M. A. Adolescent changes in the homeostatic and circadian regulation of sleep. Dev. Neurosci. 31, 276-284 (2009).

CAS PubMed PubMed Central Google Scholar

21. Shin, J. Sleep more, study less? The impact of delayed school start time on sleep and academic performance. (2018).

22. Löwen, K., Baumert, J., Kunter, M., Krauss, S. & Brunner, M. Cognitive activation in the mathematics classroom and professional competence of teachers. Results from the COACTIV project. In The COACTIV Research Program: Methodological Framework (eds Kunter, M. et al.) 79-96 (Springer, 2013).

Google Scholar

23. Neumann, K., Kauertz, A. & Fischer, H. E. Quality of instruction in science education. In Second International Handbook of Science Education (eds Fraser, B. J. et al.) 247-258 (Springer, 2012). https://doi.org/10.1007/978-1-4020-9041-7.

Chapter Google Scholar

24. Hattie, J. Visible learning: A synthesis of over 800 meta-analyses relating to achievement. (2008). https://doi.org/10.4324/9780203887332.

25. Heine, C., Briedis, K., Didi, H.-J., Haase, K. & Trost, G. Auswahl-und Eignungsfeststellungsverfahren beim Hochschulzugang in Deutschland und ausgewählten Ländern Vol. 3 (HIS-Kurzinformation, 2006).

Google Scholar

26. Estevan, I., Silva, A., Vetter, C. & Tassino, B. Short sleep duration and extremely delayed chronotypes in uruguayan youth: The role of school start times and social constraints. J. Biol. Rhythms 0748730420927601 (2020).

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Acknowledgements

We thank all the students for their engaged participation and the management of the Gymnasium Alsdorf, in particular W Bock and O Vollert, for their open support throughout the study. We also thank Dr. M Vuori-Brodowski for her contributions during wave 1, general advice and statistical support. This article has been published on bioRxiv with the following https://doi.org/10.1101/2021.07.29.452310.

Funding

Open Access funding enabled and organized by Projekt DEAL. AMB received research and travel funds from the Graduate School of Systemic Neurosciences Munich.

Author information

Author notes

1. Giulia Zerbini

Present address: Department of Medical Psychology and Sociology, University of Augsburg, Augsburg, Germany

2. Anna M. Biller

Present address: Institute of Psychology, Bundeswehr University Munich, Munich, Germany

3. Fabian Obster

Present address: Department of Business Administration, Bundeswehr University Munich, Munich, Germany

4. Till Roenneberg

Present address: Institute and Polyclinic for Occupational-, Social- and Environmental Medicine, Ludwig Maximilian University Munich, Munich, Germany

5. Eva C. Winnebeck

Present address: Chair of Neurogenetics, Faculty of Medicine, Technical University of Munich, and Institute of Neurogenomics, Helmholtz Center Munich, Munich, Germany

Authors and Affiliations

1. Institute of Medical Psychology, Ludwig Maximilian University Munich, Munich, Germany

Anna M. Biller, Carmen Molenda, Giulia Zerbini, Till Roenneberg & Eva C. Winnebeck

2. Graduate School of Systemic Neurosciences, Ludwig Maximilian University Munich, Munich, Germany

Anna M. Biller

3. Statistical Consulting Unit, Department of Statistics, Ludwig Maximilian University Munich, Munich, Germany

Fabian Obster

4. Biology Education, Faculty of Biology, Ludwig Maximilian University Munich, Munich, Germany

Christian Förtsch

Contributions

Conceptualisation: E.C.W., T.R., Methodology: E.C.W., A.M.B., Investigation: A.M.B., E.C.W., C.M., Data curation: A.M.B., C.M., Formal analysis: A.M.B., E.C.W., F.O., G.Z., C.M., Validation: E.C.W., A.M.B., F.O., G.Z., Supervision: E.C.W., Visualization: A.M.B., E.C.W., Writing-original draft: A.M.B., E.C.W., C.F., Writing-review and editing: A.M.B., E.C.W., G.Z., F.O., C.F. All authors reviewed and approved the manuscript.

Corresponding authors

Correspondence to Anna M. Biller or Eva C. Winnebeck.

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The authors declare no competing interests.

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Biller, A.M., Molenda, C., Obster, F. et al. A 4-year longitudinal study investigating the relationship between flexible school starts and grades. Sci Rep 12, 3178 (2022). https://doi.org/10.1038/s41598-022-06804-5
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DOI: https://doi.org/10.1038/s41598-022-06804-5

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