Emotional Vulnerability to Short Sleep Predicts Increases in Chronic Health Conditions Across 8 Years

Sleep disturbances across 2 weeks predict future mental healthcare utilization

STUDY OBJECTIVES

Insufficient sleep costs the US economy over $411 billion per year. However, most studies investigating the economic costs of sleep rely on one-time measures of sleep, which may be prone to recall bias and cannot capture variability in sleep. To address these gaps, we examined how sleep metrics captured from daily sleep diaries predicted medical expenditures.

METHODS

Participants were 391 World Trade Center (WTC) responders enrolled in the WTC Health Program (mean age = 54.97 years, 89% men). At baseline, participants completed 14 days of self-reported sleep and stress measures. Mean sleep, variability in sleep, and a novel measure of sleep reactivity (i.e. how much people's sleep changes in response to daily stress) were used to predict the subsequent year's medical expenditures, covarying for age, race/ethnicity, sex, medical diagnoses, and body mass index.

RESULTS

Mean sleep efficiency did not predict mental healthcare utilization. However, greater sleep efficiency reactivity to stress (b = $191.75, p = .027), sleep duration reactivity to stress (b = $206.33, p = .040), variability in sleep efficiency (b = $339.33, p = .002), variability in sleep duration (b = $260.87, p = .004), and quadratic mean sleep duration (b = $182.37, p = .001) all predicted greater mental healthcare expenditures. Together, these sleep variables explained 12% of the unique variance in mental healthcare expenditures. No sleep variables were significantly associated with physical healthcare expenditures.

CONCLUSIONS

People with more irregular sleep, more sleep reactivity, and either short or long sleep engage in more mental healthcare utilization. It may be important to address these individuals' sleep problems to improve mental health and reduce healthcare costs.

Adolescents' Daily Lives (ADL) project: an intensive longitudinal design study protocol examining the associations between physical literacy, movement behaviours, emotion regulation and mental health

INTRODUCTION

Adolescence represents a critical developmental period, with changes in emotional regulation capacities influencing physical and mental health. With less than 6% of Canadian youth currently meeting the 24-hour movement guidelines for physical activity, sleep and sedentary behaviour, there is an urgent need to understand the potential association between movement behaviours, physical literacy, emotional regulation and mental health during adolescence. Additionally, there is a need to better understand these associations among equity-deserving groups. We developed the Adolescents' Daily Lives (ADL) project to identify how, when, under what contexts and to whom to promote healthy engagement in movement behaviours to optimise youth mental health.

METHODS AND ANALYSIS

For the ADL project, we will employ a 14-day intensive longitudinal design to investigate the associations between physical literacy, movement behaviours, emotion regulation and mental health among a diverse sample of 120 adolescents (ages 13-17 years) living in the Greater Victoria Area, British Columbia, Canada. A comprehensive baseline survey and movement competence test, assessing physical and mental well-being, 24-hour movement behaviours (ie, physical activity, sleep and sedentary behaviours) and physical literacy, will be accompanied by daily diary surveys and accelerometer-based movement tracking (ie, Fitbit Inspire 3) to assess daily fluctuations in movement behaviour, emotional regulation and mood. Multivariate analyses, including multilevel modelling, multilevel structural equation modelling and Bayesian hierarchical continuous-time SEM, will be used to model the repeated measures data and understand the simultaneous variations in daily movement behaviours, emotion regulation and mental health.

ETHICS AND DISSEMINATION

The ADL project received ethical approval from the University of Victoria Behavioural Research Ethics Board (protocol #22-0262). Study participation is voluntary, and data collection will be anonymised to protect participant privacy and confidentiality. Research findings will be shared through academic publications and conference proceedings. Through knowledge mobilisation resources, cocreated with the youth community advisory board, relevant findings will be shared directly with the wider community of adolescents.

Monitoring Daily Sleep, Mood, and Affect Using Digital Technologies and Wearables: A Systematic Review

Background: Sleep and affective states are closely intertwined. Nevertheless, previous methods to evaluate sleep-affect associations have been limited by poor ecological validity, with a few studies examining temporal or dynamic interactions in naturalistic settings. Objectives: First, to update and integrate evidence from studies investigating the reciprocal relationship between daily sleep and affective phenomena (mood, affect, and emotions) through ambulatory and prospective monitoring. Second, to evaluate differential patterns based on age, affective disorder diagnosis (bipolar, depression, and anxiety), and shift work patterns on day-to-day sleep-emotion dyads. Third, to summarise the use of wearables, actigraphy, and digital tools in assessing longitudinal sleep-affect associations. Method: A comprehensive PRISMA-compliant systematic review was conducted through the EMBASE, Ovid MEDLINE(R), PsycINFO, and Scopus databases. Results: Of the 3024 records screened, 121 studies were included. Bidirectionality of sleep-affect associations was found (in general) across affective disorders (bipolar, depression, and anxiety), shift workers, and healthy participants representing a range of age groups. However, findings were influenced by the sleep indices and affective dimensions operationalised, sampling resolution, time of day effects, and diagnostic status. Conclusions: Sleep disturbances, especially poorer sleep quality and truncated sleep duration, were consistently found to influence positive and negative affective experiences. Sleep was more often a stronger predictor of subsequent daytime affect than vice versa. The strength and magnitude of sleep-affect associations were more robust for subjective (self-reported) sleep parameters compared to objective (actigraphic) sleep parameters.

Day-to-day directional relationships between sleep duration and negative affect

OBJECTIVE

There is a strong association between sleep disturbance and negative affect. However, the day-to-day directional connections between sleep and negative affect remain unclear. We examined day-to-day relationships between sleep duration and negative affect in community adults.

METHODS

Participants were two subsamples of the Midlife in the United States Study (Sample 1: n = 2,022; Sample 2: n = 782). Daily negative affect and previous night sleep duration were assessed via end-of-day telephone interviews for eight days. Random intercept cross-lagged panel models tested sleep duration as a predictor of next-day negative affect and vice versa, controlling for age, gender, and race.

RESULTS

In both samples, shorter sleep duration predicted higher next-day negative affect, but daily negative affect was not a significant predictor of upcoming-night sleep duration. Follow-up analyses indicated that the relationship between sleep duration and negative affect was nonlinear. Sleeping fewer than 7.5 hours or more than 10.5 hours was associated with greater next-day negative affect than sleeping between 7.5 and 10.5 hours.

CONCLUSIONS

In two large samples of community adults, sleep duration unidirectionally predicted higher next-day negative affect, and this relationship was nonlinear. Sleeping at least 7.5 hours and no more than 10.5 hours appeared to be an optimal range associated with lowest next-day negative affect.

Daily reactivity to stress and sleep disturbances: unique risk factors for insomnia

STUDY OBJECTIVES

To naturalistically measure sleep disturbances following stress exposure (i.e. sleep reactivity) and stress responses following sleep disturbances (i.e. stress reactivity) at the daily level and prospectively examine these reactivity measures as individual risk factors for insomnia.

METHODS

The study assessed 392 nurses' sleep and stress for 14 days using daily diaries and actigraphy. Self-reported insomnia symptoms were assessed at the end of the 14 days, as well as 6 and 11 months later.

RESULTS

In multilevel modeling, while negative fixed effects indicated that shorter total sleep time (TST) and lower sleep efficiency led to greater stress and vice versa, significant random effects indicated individual variability in sleep reactivity and stress reactivity. In latent score change modeling, greater sleep reactivity (lower diary-determined sleep efficiency following greater stress) and greater stress reactivity (greater stress following shorter diary-determined TST) at baseline were associated with greater insomnia symptoms at 11 months (b = 10.34, p = .026; b = 7.83, p = .03). Sleep reactivity and stress reactivity also interacted to predict insomnia symptoms, such that sleep reactivity was significantly associated with insomnia symptoms for those with high (+1 SD) stress reactivity (b = 17.23, p = .001), but not for those with low (-1 SD) stress reactivity (b = 5.16, p = .315).

CONCLUSIONS

Baseline stress reactivity and sleep reactivity independently as well as jointly predict greater insomnia symptoms 11 months later. The findings delineate processes underlying the stress-diathesis model of insomnia and highlight the utility of longitudinal and naturalistic measures of sleep and stress reactivity.

Associations Between Intraindividual Variability in Sleep and Daily Positive Affect

Recent research highlights a variety of negative outcomes associated with intraindividual variability in positive affect (PA) and in sleep. Thus, this study examined the associations of variability in multiple dimensions of sleep (quality, duration, wake after sleep onset, bedtime, rise-time) with mean and variability in PA. For 7 days, morning and evening surveys were collected online from two separate samples: community-based adults (N = 911) and university students (N = 322). Regression analyses revealed that across both samples, people with more variable sleep quality exhibited greater fluctuations in PA throughout the week and, surprisingly, higher levels of PA on average. In the community sample only, individuals with more variable sleep duration had lower and more variable PA. Findings suggest that fluctuations in sleep quality and duration are linked with daily PA outcomes, which is important to consider as technological advances and modern demands make inadequate and irregular sleep increasingly common.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-021-00082-6.

Raw scores on subjective sleepiness, fatigue, and vigor metrics consistently define resilience and vulnerability to sleep loss

STUDY OBJECTIVES

Although trait-like individual differences in subjective responses to sleep restriction (SR) and total sleep deprivation (TSD) exist, reliable characterizations remain elusive. We comprehensively compared multiple methods for defining resilience and vulnerability by subjective metrics.

METHODS

41 adults participated in a 13-day experiment:2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue (POMS-F) and Vigor (POMS-V) were administered every 2h. Three approaches (Raw Score [average SR score], Change from Baseline [average SR minus average baseline score], and Variance [intraindividual SR score variance]), and six thresholds (±1 standard deviation, and the highest/lowest scoring 12.5%, 20%, 25%, 33%, 50%) categorized Resilient/Vulnerable groups. Kendall's tau-b correlations compared the group categorization's concordance within and between KSS, POMS-F, and POMS-V scores. Bias-corrected and accelerated bootstrapped t-tests compared group scores.

RESULTS

There were significant correlations between all approaches at all thresholds for POMS-F, between Raw Score and Change from Baseline approaches for KSS, and between Raw Score and Variance approaches for POMS-V. All Resilient groups defined by the Raw Score approach had significantly better scores throughout the study, notably including during baseline and recovery, whereas the two other approaches differed by measure, threshold, or day. Between-measure correlations varied in strength by measure, approach, or threshold.

CONCLUSION

Only the Raw Score approach consistently distinguished Resilient/Vulnerable groups at baseline, during sleep loss, and during recovery‒‒we recommend this approach as an effective method for subjective resilience/vulnerability categorization. All approaches created comparable categorizations for fatigue, some were comparable for sleepiness, and none were comparable for vigor. Fatigue and vigor captured resilience/vulnerability similarly to sleepiness but not each other.

Cognitive throughput and working memory raw scores consistently differentiate resilient and vulnerable groups to sleep loss

STUDY OBJECTIVES

Substantial individual differences exist in cognitive deficits due to sleep restriction (SR) and total sleep deprivation (TSD), with various methods used to define such neurobehavioral differences. We comprehensively compared numerous methods for defining cognitive throughput and working memory resiliency and vulnerability.

METHODS

41 adults participated in a 13-day experiment: 2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The Digit Symbol Substitution Test (DSST) and Digit Span Test (DS) were administered every 2h. Three approaches (Raw Score [average SR performance], Change from Baseline [average SR minus average baseline performance], and Variance [intraindividual variance of SR performance]), and six thresholds (±1 standard deviation, and the best/worst performing 12.5%, 20%, 25%, 33%, 50%) classified Resilient/Vulnerable groups. Kendall's tau-b correlations compared the group categorizations' concordance within and between DSST number correct and DS total number correct. Bias-corrected and accelerated bootstrapped t-tests compared group performance. .

RESULTS

The approaches generally did not categorize the same participants into Resilient/Vulnerable groups within or between measures. The Resilient groups categorized by the Raw Score approach had significantly better DSST and DS performance across all thresholds on all study days, while the Resilient groups categorized by the Change from Baseline approach had significantly better DSST and DS performance for several thresholds on most study days. By contrast, the Variance approach showed no significant DSST and DS performance group differences.

CONCLUSION

Various approaches to define cognitive throughput and working memory resilience/vulnerability to sleep loss are not synonymous. The Raw Score approach can be reliably used to differentiate resilient and vulnerable groups using DSST and DS performance during sleep loss.