Is the transition from primary to secondary school a risk factor for energy balance-related behaviours? A systematic review

OBJECTIVE

The substantial changes in the physical and social environment during the transition from primary to secondary school may significantly impact adolescents' energy balance-related behaviours (i.e. dietary behaviour, sedentary behaviour, sleep behaviour and physical activity (PA)). This is the first review systematically summarising evidence on changes in four energy balance-related behaviours of adolescents across the school transition from primary to secondary school.

DESIGN

For this systematic review, the electronic databases Embase, PsycINFO and SPORTDiscus were searched for relevant studies from inception to August 2021. PubMed was searched for relevant studies from inception to September 2022. Inclusion criteria were: (i) longitudinal studies reporting; (ii) one or more energy balance-related behaviours; and (iii) across the school transition, that is, with measurement(s) during both primary and secondary school.

SETTING

Transition from primary to secondary school.

PARTICIPANTS

Adolescents across the transition from primary to secondary school.

RESULTS

Thirty-four studies were eligible. We found strong evidence for an increase in sedentary time, moderate evidence for a decrease in fruit and vegetable consumption, and inconclusive evidence for a change in total, light, and moderate-to-vigorous PA, active transport, screen time, unhealthy snack consumption, and sugar-sweetened beverages consumption among adolescents across the school transition.

CONCLUSIONS

During the transition from primary to secondary school, sedentary time and fruit and vegetable consumption tend to change unfavourably. More high-quality, longitudinal research is needed specifically on changes in energy balance-related behaviour across the school transition, especially regarding sleep behaviour. (Prospero registration: CRD42018084799).

Sleep, circadian biology and skeletal muscle interactions: Implications for metabolic health

There currently exists a modern epidemic of sleep loss, triggered by the changing demands of our 21st century lifestyle that embrace 'round-the-clock' remote working hours, access to energy-dense food, prolonged periods of inactivity, and on-line social activities. Disturbances to sleep patterns impart widespread and adverse effects on numerous cells, tissues, and organs. Insufficient sleep causes circadian misalignment in humans, including perturbed peripheral clocks, leading to disrupted skeletal muscle and liver metabolism, and whole-body energy homeostasis. Fragmented or insufficient sleep also perturbs the hormonal milieu, shifting it towards a catabolic state, resulting in reduced rates of skeletal muscle protein synthesis. The interaction between disrupted sleep and skeletal muscle metabolic health is complex, with the mechanisms underpinning sleep-related disturbances on this tissue often multifaceted. Strategies to promote sufficient sleep duration combined with the appropriate timing of meals and physical activity to maintain circadian rhythmicity are important to mitigate the adverse effects of inadequate sleep on whole-body and skeletal muscle metabolic health. This review summarises the complex relationship between sleep, circadian biology, and skeletal muscle, and discusses the effectiveness of several strategies to mitigate the negative effects of disturbed sleep or circadian rhythms on skeletal muscle health.

Effect of sleep and fatigue on cardiovascular performance in young, healthy subjects

Physical performance could be negatively affected by sleep deficiency and fatigue. The present study assesses the role of sleep quality, fatigue and motivation on cardiovascular performance (VO₂peak, Wmax, and HRmax) in a sample of active young subjects. The current study is a cross-sectional design. Ninety-six university students (males 54.2%; 21.5 ± 2.9 yrs) completed an incremental exercise test on a bicycle ergometer. Sleep, fatigue, and motivation were assessed with the Pittsburgh Sleep Quality Index and two visual analogue scales, respectively. Differences in VO₂peak, Wmax, HRmax, self-perceived fatigue and motivation were compared between good and bad sleepers and sleep duration >/<7.5 h, while regression analysis defined the predictors of VO₂peak, Wmax, and HRmax_. In the male sample, good and bad sleepers' differences were significant only for self-perceived fatigue (p = 0.04). The female sample showed no statistically significant differences between good and bad sleepers and different sleep durations. In the male sample, linear regression analysis showed a significant inverse correlation between Wmax and the PSQI score (-0.4, p = 0.004). The stepwise regression model indicated that sleep (β = -0.3, p = 0.02) was a significant predictor of VO₂peak in males accounting for 20% of the variance, whereas physical performance seems more affected by fatigue (β = -0.4, p = 0.03) in females. In conclusion, chronic inadequate and self-reported sleep quality seems to be one of the factors compromising cardiovascular performance in males.

Effects of sleep self-monitoring via app on subjective sleep markers in student athletes

As sleep problems are highly prevalent among university students and competitive athletes, and the application of commercial sleep technologies may be either useful or harmful, this study investigated the effects of a 2-week sleep self-monitoring on the sleep of physically active university students (n = 98, 21 ± 1.7 years). Two intervention groups used a free sleep app (Sleep Score; SleepScore Labs™, Carlsbad, CA, USA: n = 20 or Sleep Cycle; Sleep Cycle AB, Gothenburg, Sweden: n = 24) while answering online sleep diaries. They used the app analysis function in week 1 and the 'smart alarm' additionally in week 2. As controls, one group answered the online sleep diary without intervention (n = 21) and another the pre-post questionnaires only (n = 33). Facets of subjective sleep behaviour and the role of bedtime procrastination were analysed. Multilevel models did not show significant interactions, indicating intervention effects equal for both app groups. Sleep Cycle users showed trends toward negative changes in sleep behaviour, while the online sleep diary group showed more, tendentially positive, developments. Bedtime procrastination was a significant predictor of several variables of sleep behaviour and quality. The results indicate neither benefits nor negative effects of app-based sleep self-tracking. Thus, student athletes do not seem to be as susceptible to non-validated sleep technologies as expected. However, bedtime procrastination was correlated with poor sleep quality and should be addressed in sleep intervention programmes.

Bidirectional associations between sedentary time and sleep duration among 12- to 14-year-old adolescents

Background

The aim of this study was to investigate bidirectional associations between (prolonged) sitting time and sleep duration in 12- to 14-year-old adolescents using a between-subjects and within-subjects analyses approach.

Methods

Observational data were used from 108 adolescents (53% girls; mean age 12.9 (SD 0.7) years) from six schools in Flanders, Belgium. The Axivity AX3 triaxial accelerometer, worn on the thigh, was used to assess daily total sitting time and daily time spent in sedentary bouts of ≥30 min (as a proxy for prolonged sitting time). The Fitbit Charge 3 was used to assess nightly sleep duration. Both monitors were worn on schooldays only (ranging from 4 to 5 days). Linear mixed models were conducted to analyse the associations, resulting in four models. In each model, the independent variable (sleep duration, sitting time or prolonged sitting time) was included as within- as well as between-subjects factor.

Results

Within-subjects analyses showed that when the adolescents sat more and when the adolescents spent more time sitting in bouts of ≥30 min than they usually did on a given day, they slept less during the following night (p = 0.01 and p = 0.05 (borderline significant), respectively). These associations were not significant in the other direction. Between-subjects analyses showed that adolescents who slept more on average, spent less time sitting (p = 0.006) and less time sitting in bouts of ≥30 min (p = 0.004) compared with adolescents who slept less on average. Conversely, adolescents who spent more time sitting on average and adolescents who spent more time sitting in bouts of ≥30 min on average, slept less (p = 0.02 and p = 0.003, respectively).

Conclusions

Based on the between-subjects analyses, interventions focusing on reducing or regularly breaking up sitting time could improve adolescents’ sleep duration on a population level, and vice versa. However, the within-subjects association was only found in one direction and suggests that to sleep sufficiently during the night, adolescents might limit and regularly break up their sitting time the preceding day.

Trial registration

Data have been used from our trial registered at ClinicalTrials.gov (NCT04327414; registered on March 11, 2020).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11694-9.

Update on school-based sleep education programs: how far have we come and what has Australia contributed to the field?

OBJECTIVE

School-based sleep education programs help to promote sleep health information to many children and adolescents. The aim of this systematic review was to identify and describe school-based sleep education programs, to update previous reviews and identify recent advances and improvements in this field worldwide.

METHODS

Four electronic databases were searched. Eligibility criteria included children aged 5-18 years, sleep education intervention conducted in a school setting, and at least one pre-post-measure of a sleep variable.

RESULTS

A total of 32 articles met eligibility criteria with Australian sleep researchers constituting ∼ one quarter of these studies. Studies dated from 2007 to 2020 with sample sizes ranging from 9 to 3713 students. The majority of participants were high school students and predominantly female. Education programs generally took 4-6 weeks and content was consistent across studies. Overall, exposure to sleep education increased sleep knowledge, however changes in sleep behaviour variables and secondary outcome variables (eg, mental health; cognitive function; sleep hygiene practices) presented varied results. Studies conducted since 2015 were more likely to be randomised controlled trials and to include more interactive, online designs utilising innovative content such as mindfulness.

CONCLUSIONS

An exponential growth in school sleep education programs was identified since 2016. Future studies should consider utilising objective sleep measures, longer-term follow-ups, innovative delivery methods, and stronger attempts at implementing a knowledge-to-action approach for more sustainable programs.

Sleep patterns, genetic susceptibility, and incident cardiovascular disease: a prospective study of 385 292 UK biobank participants

AIMS

To quantify the association of combined sleep behaviours and genetic susceptibility with the incidence of cardiovascular disease (CVD).

METHODS AND RESULTS

This study included 385 292 participants initially free of CVD from UK Biobank. We newly created a healthy sleep score according to five sleep factors and defined the low-risk groups as follows: early chronotype, sleep 7-8 h per day, never/rarely insomnia, no snoring, and no frequent excessive daytime sleepiness. Weighted genetic risk scores of coronary heart disease (CHD) or stroke were calculated. During a median of 8.5 years of follow-up, we documented 7280 incident CVD cases including 4667 CHD and 2650 stroke cases. Compared to those with a sleep score of 0-1, participants with a score of 5 had a 35% (19-48%), 34% (22-44%), and 34% (25-42%) reduced risk of CVD, CHD, and stroke, respectively. Nearly 10% of cardiovascular events in this cohort could be attributed to poor sleep pattern. Participants with poor sleep pattern and high genetic risk showed the highest risk of CHD and stroke.

CONCLUSION

In this large prospective study, a healthy sleep pattern was associated with reduced risks of CVD, CHD, and stroke among participants with low, intermediate, or high genetic risk.

Artificial light at night affects sleep behaviour differently in two closely related songbird species

Artificial light at night (ALAN) or light pollution is an increasing and worldwide problem. There is growing concern that because of the disruption of natural light cycles, ALAN may pose serious risks for wildlife. While ALAN has been shown to affect many aspects of animal behaviour and physiology, few studies have experimentally studied whether individuals of different species in the wild respond differently to ALAN. Here, we investigated the effect of ALAN on sleep behaviour in two closely related songbird species inhabiting the same study area and roosting/breeding in similar nest boxes. We experimentally exposed free-living great tits (Parus major) and blue tits (Cyanistes caeruleus) to artificial light inside their nest boxes and observed changes in their sleep behaviour compared to the previous night when the nest boxes were dark. In line with previous studies, sleep behaviour of both species did not differ under dark conditions. ALAN disrupted sleep in both great and blue tits. However, compared to blue tits, great tits showed more pronounced effects and more aspects of sleep were affected. Light exposed great tits entered the nest boxes and fell asleep later, woke up and exited the nest boxes earlier, and the total sleep amount and sleep percentage were reduced. By contrast, these changes in sleep behaviour were not found in light exposed blue tits. Our field experiment, using exactly the same light manipulation in both species, provides direct evidence that two closely related species respond differently to ALAN, while their sleep behaviour under dark conditions was similar. Our research suggests that findings for one species cannot necessarily be generalised to other species, even closely-related species. Furthermore, species-specific effects could have implications for community dynamics.

Sleep/wake behaviours of elite athletes from individual and team sports

Sleep is an essential component for athlete recovery due to its physiological and psychological restorative effects, yet few studies have explored the habitual sleep/wake behaviour of elite athletes. The aims of the present study were to investigate the habitual sleep/wake behaviour of elite athletes, and to compare the differences in sleep between athletes from individual and team sports. A total of 124 (104 male, 20 female) elite athletes (mean ± s: age 22.2 ± 3.0 years) from five individual sports and four team sports participated in this study. Participants' sleep/wake behaviour was assessed using self-report sleep diaries and wrist activity monitors for a minimum of seven nights (range 7-28 nights) during a typical training phase. Mixed-effects analyses of variances were conducted to compare the differences in the sleep/wake behaviour of athletes from two sport types (i.e. individual and team). Overall, this sample of athletes went to bed at 22:59 ± 1.3, woke up at 07:15 ± 1.2 and obtained 6.8 ± 1.1 h of sleep per night. Athletes from individual sports went to bed earlier, woke up earlier and obtained less sleep (individual vs team; 6.5 vs 7.0 h) than athletes from team sports. These data indicate that athletes obtain well below the recommended 8 h of sleep per night, with shorter sleep durations existing among athletes from individual sports.