The interrelationship of sleep and depression: new answers but many questions remain

Insomnia is more likely to persist than remit after a time of stress and uncertainty: a longitudinal cohort study examining trajectories and predictors of insomnia symptoms

STUDY OBJECTIVES

The study aimed to characterize insomnia symptom trajectories over 12 months during a time of stress and uncertainty, the coronavirus disease 2019 (COVID-19) pandemic. It also aimed to investigate sleep and psychological predictors of persistent insomnia symptoms.

METHODS

This longitudinal cohort study comprised 2069 participants with and without insomnia symptoms during the first year of the pandemic. Participants completed online surveys investigating sleep, insomnia, and mental health at four timepoints over 12 months (April 2020-May 2021). Additional trait-level cognitive/psychological questionnaires were administered at 3 months only.

RESULTS

Six distinct classes of insomnia symptoms emerged: (1) severe persistent insomnia symptoms (21.65%), (2) moderate persistent insomnia symptoms (32.62%), (3) persistent good sleep (32.82%), (4) severe insomnia symptoms at baseline but remitting over time (2.27%), (5) moderate insomnia symptoms at baseline but remitting over time (7.78%), and (6) good sleep at baseline but deteriorating into insomnia symptoms over time (2.85%). Persistent insomnia trajectories were predicted by high levels of sleep reactivity, sleep effort, pre-sleep cognitive arousal, and depressive symptoms at baseline. A combination of high sleep reactivity and sleep effort reduced the odds of insomnia remitting. Higher sleep reactivity also predicted the deterioration of good sleep into insomnia symptoms over 12 months. Lastly, intolerance of uncertainty emerged as the only trait-level cognitive/psychological predictor of insomnia trajectory classes.

CONCLUSIONS

Insomnia was more likely to persist than remit over the first year of the COVID-19 pandemic. Addressing sleep reactivity and sleep effort appears critical for reducing insomnia persistence rates after times of stress and uncertainty.

Infant sleep patterns and maternal postpartum fatigue: A cross-sectional study

AIM

Unpredictable nature of infant sleep-wake cycles go against that of the adult sleep. Maternal sleep quality is acknowledged as a main factor to postpartum fatigue. This study explored the effects of infant sleep patterns on maternal postpartum fatigue.

METHODS

A total of 319 mothers of infants from two teaching hospitals in the Nantong City were enrolled. Maternal fatigue status and infant sleep patterns were assessed by the Fatigue Scale (FS-14) and the Brief Infant Sleep Questionnaire (BISQ). Generalized estimating equation analyses were perform to explored the effect of infant sleep patterns on maternal fatigue.

RESULTS

Sleep-onset time later than 10 p.m., and nocturnal wakefulness more than 60 min were risk factors of postpartum fatigue. After controlling the confounding variables, infant sleep patterns were still the determinant factors of postpartum fatigue.

CONCLUSION

Infant sleep pattern is a factor of maternal postpartum fatigue. It is recommended to promote infant sleep so as to improve maternal sleep quality and finally alleviate postpartum fatigue.

DeepSleep convolutional neural network allows accurate and fast detection of sleep arousal

Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with symptoms such as sympathetic activation, non-restorative sleep, and daytime sleepiness. Currently, sleep arousals are mainly annotated by human experts through looking at 30-second epochs (recorded pages) manually, which requires considerable time and effort. Here we present a deep learning approach for automatically segmenting sleep arousal regions based on polysomnographic recordings. Leveraging a specific architecture that 'translates' input polysomnographic signals to sleep arousal labels, this algorithm ranked first in the "You Snooze, You Win" PhysioNet Challenge. We created an augmentation strategy by randomly swapping similar physiological channels, which notably improved the prediction accuracy. Our algorithm enables fast and accurate delineation of sleep arousal events at the speed of 10 seconds per sleep recording. This computational tool would greatly empower the scoring process in clinical settings and accelerate studies on the impact of arousals.

Sleep disturbances and disability following work-related injury and illness: Examining longitudinal relationships across three follow-up waves

Despite the high burden of sleep disturbances among the general population, there is limited information on prevalence and impact of poor sleep among injured workers. This study: (a) estimated the prevalence of sleep disturbance following work-related injury; and (b) examined the longitudinal association between sleep disturbances and disability/functioning, accounting for reciprocal relationships and mental illness. Longitudinal survey data were collected from workers' compensation claimants with a time-loss claim in Victoria, Australia (N = 700). Surveys were conducted at baseline, 6 months and 12 months. Sleep disturbance was measured using the Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaire. Disability/functioning was based on self-reported activity limitations, participation restrictions and emotional functioning. Path models examined the association between disability/functioning and sleep. Mean sleep disturbance T-scores were 55.2 (SD 11.4) at 6 months, with 36.4% of the sample having a T-score of 60+. Longitudinal relationships were observed between disability (specifically, emotional functioning) and sleep disturbances across successive follow-up waves. For example, each unit increase in T2 emotional functioning (five-point scale) was associated with a 1.1 unit increase in T3 sleep disturbance (approximately 29-76 scale). Cross-lagged path models found evidence of a reciprocal relationship between disability and sleep, although adjustment for mental illness attenuated the estimates to the null. In conclusion, sleep disturbances are common among workers' compensation claimants with work injuries/illnesses. Given the links between some dimensions of disability, mental health and sleep disturbances, the findings have implications for the development of interventions that target the high prevalence of sleep problems among working populations.