Highlights Social support is inversely related to depression, anxiety, irritability, sleep quality and loneliness during quarantine. Higher levels of social support reduces the risk of depression during quarantine. Higher levels of social support increases sleep quality during quarantine.

The COVID-19 pandemic has brought about profound changes to social behaviour. While calls to identify mental health effects that may stem from these changes should be heeded, there is also a need to examine potential changes with respect to health behaviours. Media reports have signalled dramatic shifts in sleep, substance use, physical activity and diet, which may have subsequent downstream mental health consequences. We briefly discuss the interplay between health behaviours and mental health, and the possible changes in these areas resulting from anti-pandemic measures. We also highlight a call for greater research efforts to address the short and long-term consequences of changes to health behaviours.

No abstract

Later chronotype young adolescents are at risk of increased BMI and poorer dietary behaviors. Although short sleep duration, but not sleep efficiency, was also an independent risk factor for increased BMI, different mechanisms may be driving the late chronotype and shorter sleep duration associations with BMI in this age group. Sleep hygiene education may help adolescents to better understand the impact of sleeping habits on physical health.

A systematic review and a meta-analysis were conducted to examine the overall prevalence of psychological health outcomes during COVID-19. Seven databases were systematically searched to include studies reporting on at least one psychological outcome. The pooled prevalence of primary psychological outcomes was 26% (95%CI: 21–32). Pooled prevalence for symptoms of PTSD was 33% (0–86), anxiety 28% (21–36), stress 27% (14–43), and depression 22% (13–33). The prevalence of psychological outcomes was similar in healthcare workers and in the general population (34% [24–44] and 33% [27–40] respectively). High prevalence figures support the importance of ensuring adequate provision of resources for mental health.

Study ObjectivesThe majority of adolescent sleep research has utilized self-reported sleep duration and some have based information on a solitary question. Whilst some have claimed to have validated sleep survey data with objective actigraphy measures in adolescents, the statistical approach applied only demonstrates the strength of the association between subjective and objective sleep duration data and does not reflect if these different methods actually agree.MethodsData were collected as part of the Midlands Adolescents Schools Sleep Education Study (MASSES). Adolescents (n=225) aged 11-13 years provided estimates for weekday, weekend and combined sleep duration based on self-reported survey data, a 7-day sleep diary, and wrist-worn actigraphy.ResultsWe assessed the strength of the relationship as well as agreement levels between subjective and objectively determined sleep duration (weekday, weekend and combined). Subjective diary sleep duration was significantly correlated with actigraphy estimates for weekday and weekend sleep duration r=0.30, p≤0.001 and r=0.31, p≤0.001 respectively. Pitman’s test demonstrated no significant difference in the variance between weekend sleep duration (r=0.09, p=0.16) and combined sleep duration (r=0.12, p=0.08) indicating acceptable agreement between actigraphy and sleep diary sleep duration only. Self-reported sleep duration estimates (weekday, weekend and combined) did not agree with actigraphy determined sleep duration.ConclusionsSleep diaries are a cost-effective alternative to survey/questionnaire data. Self-reported measures of sleep duration in adolescents do not agree with actigraphy measures and should be avoided where possible. Previous adolescent sleep studies that have utilized self-reported survey data may not provide a complete representation of sleep on the outcome measure of interest.

BackgroundThe importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science.ObjectiveThe objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality.MethodsActigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM).ResultsDeep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional linear regression. CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional linear regression (0.6463).ConclusionsDeep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.

OBJECTIVETo examine the association between total sleep duration and the prevalence of metabolic syndrome (MetSyn) in older Chinese.RESEARCH DESIGN AND METHODSCross-sectional analysis of baseline data from the Guangzhou Biobank Cohort Study (GBCS) was performed. Participants (n = 29,333) were aged ≥50 years. Risk of MetSyn and its components were identified for self-reported total sleep duration.RESULTSParticipants reporting long (≥9 h) and short (<6 h) total sleep duration had increased odds ratio (OR) of 1.18 (95% CI 1.07–1.30) and 1.14 (1.05–1.24) for the presence of MetSyn, respectively. The relationship remained in long sleepers (OR 1.21 [1.10–1.34]) but diminished in short sleepers (0.97 [0.88–1.06]) after full adjustment.CONCLUSIONSLong sleep duration was associated with greater risk of MetSyn in older Chinese. Confirmation through longitudinal studies is needed. The mechanisms mediating the link between long sleep duration and MetSyn require further investigation.