Comparisons of actigraphy findings between studies are challenging given differences between brand-specific algorithms. This issue may be minimized by using open-source algorithms. However, the accuracy of actigraphy-derived sleep parameters processed in open-source software needs to be assessed against polysomnography (PSG). Middle-aged adults from the Raine Study (n = 835; F 58%; Age 56.7 ± 5.6 years) completed one night of in-laboratory PSG and concurrent actigraphy (GT3X+ ActiGraph). Actigraphic measures of total sleep time (TST) were analyzed and processed using the open-source R-package GENEActiv and GENEA data in R (GGIR) with and without a sleep diary and additionally processed using proprietary software, ActiLife, for comparison. Bias and agreement (intraclass correlation coefficient) between actigraphy and PSG were examined. Common PSG and sleep health variables associated with the discrepancy between actigraphy, and PSG TST were examined using linear regression. Actigraphy, assessed in GGIR, with and without a sleep diary overestimated PSG TST by (mean ± SD) 31.0 ± 50.0 and 26.4 ± 69.0 minutes, respectively. This overestimation was greater (46.8 ± 50.4 minutes) when actigraphy was analyzed in ActiLife. Agreement between actigraphy and PSG TST was poor (ICC = 0.27-0.44) across all three methods of actigraphy analysis. Longer sleep onset latency and longer wakefulness after sleep onset were associated with overestimation of PSG TST. Open-source processing of actigraphy in a middle-aged community population, agreed poorly with PSG and, on average, overestimated TST. TST overestimation increased with increasing wakefulness overnight. Processing of actigraphy without a diary in GGIR was comparable to when a sleep diary was used and comparable to actigraphy processed with proprietary algorithms in ActiLife.
Emotions show a certain degree of continuity during the day, a quality referred to as emotional inertia, and that is typically elevated in depression. Little is known however about the extent to which our emotional experiences may or may not also persist overnight. Do our feelings continue from evening to morning or not? And how is this related to depressive symptoms and sleep quality? In an experience sampling studies in healthy subjects (ns = 123) we investigated (1) to what extent people's mood, in terms of positive and negative affect, in the morning, after a night of sleep, can be predicted from their mood of the evening before, and whether this is moderated by (2) depressive symptom severity or (3) subjective sleep quality. Results showed that morning negative affect could be strongly predicted based on previous evening negative affect, whilst this carry-over effect was not observed for positive affect, indicating that negative affect shows a general tendency to persist overnight, while positive affect did not show such continuity. The overnight prediction of both negative and positive affect was not moderated by level of depressive symptoms, nor by subjective sleep quality.
Study objectives: The aim of this study was to; (1) explore whether adolescents use technology as distraction from negative thoughts before sleep, (2) assess whether adolescents who perceive having a sleep problem use technology as distraction more compared to adolescents without sleep complaints, and (3) collect qualitative information about which devices and apps adolescents use as a distraction.
Methods: This study used a mixed-methods cross-sectional design, where 684 adolescents (M = 15.1, SD = 1.2, 46% female) answered both quantitative and qualitative questions about their sleep (perceived sleep problem, sleep onset time (SOT), and sleep onset latency [SOL]) and technology use as distraction from negative thoughts.
Results: The majority of adolescents answered "yes" or "sometimes" using technology as a distraction from negative thoughts (23.6% and 38.4%). Adolescents who answered "yes" to using technology as distraction were more likely to report having a sleep problem, longer SOL, and later SOT, compared to adolescents who answered "no". The most popular device to distract was the phone, because of its availability, and the most common apps used for distraction included YouTube, Snapchat, and music apps.
Conclusions: This study shows that many adolescents use technology to distract themselves from negative thoughts, which may help them manage the sleep-onset process. Thus, distraction may be one mechanism explaining how sleep affects technology use, rather than vice versa.
The COVID-19 pandemic altered work environments of nurses, yielding high rates of stress and burnout. Potential protective factors, including effective sleep, may influence psychological health and wellbeing. Evidence about sleep in nurses may help develop interventions that mitigate burnout and poor psychological outcomes. A cross sectional survey was distributed across three hospitals to nurses in New York City (NYC). During the first wave of the pandemic (March-April 2020), NYC had the highest incidence of laboratory-confirmed COVID-19 cases (915/100 000) and half of all COVID-related deaths nationwide. Multivariable logistic regression was used to determine associations between Pittsburgh Sleep Quality Index (PSQI) global sleep score, PSQI sleep dimensions, and psychological health (burnout, depression, anxiety, and compassion fatigue), unadjusted and then controlling for individual and professional characteristics. More than half of the participants reported burnout (64%), depression, (67%), and anxiety (77%). Eighty percent of participants had PSQI global scores >5 (poor sleep) (mean 9.27, SD 4.14). Respondents reporting good sleep (PSQI ≤ 5) had over five times the odds of no burnout (OR: 5.65, 95% CI: 2.60, 12.27); increased odds of screening negative for depression (OR: 6.91, 95% CI: 3.24, 14.72), anxiety (OR: 10.75, 95% CI: 4.22, 27.42), and compassion fatigue (OR: 7.88, 95% CI: 1.97, 31.51). Poor subjective sleep quality PSQI subcomponent was associated with burnout (OR: 2.21, 95% CI: 1.41, 3.48) but sleep duration subcomponent was not (OR: 0.84, 95% CI: 0.59, 1.19). Daytime dysfunction was significantly associated with all psychological outcomes. Sleep disturbances and medications yielded higher anxiety odds. Overall, sleep quality appears more strongly related to burnout than sleep duration in nurses working during the COVID-19 pandemic. Sleep interventions should target individual sleep dimensions in nurses.
Study objectives: The influence of biological sex on sleep inertia symptoms is currently unknown. We investigated the role of sex differences in the subjective experience and objective cognitive manifestation of sleep inertia following nighttime awakenings.
Methods: Thirty-two healthy adults (16 female, 25.91 ± 5.63 years) completed a 1-week at-home study with one experimental night during which sleep was measured by polysomnography and participants were awakened during their habitual sleep time. Participants completed a psychomotor vigilance task, Karolinska Sleepiness Scale (KSS), visual analog mood scales, and a descending subtraction task (DST) prior to sleep (baseline) and at 2, 12, 22, and 32 min after awakening. A series of mixed-effects models with Bonferroni-corrected post hoc tests were used to examine the main effects of test bout and sex, and their interaction, with a random effect of participant, and order of wake-up and sleep history as covariates.
Results: All outcomes except for percent correct on the DST showed a significant main effect of test bout, with worse performance after waking compared to baseline (all ps < .003). Significant effects of sex (p = .002) and sex × test bout (p = .01; R2M = 0.49, R2C = 0.69) were observed for KSS, with females reporting a greater increase in sleepiness from baseline to after waking compared to males.
Conclusions: These results suggest that while females reported feeling sleepier than males following nighttime awakenings, their cognitive performance was comparable. Future research is needed to determine whether perceptions of sleepiness influence decision-making during the transition from sleep to wakefulness.
Study objectives: The psychomotor vigilance test (PVT), a 10-min one-choice reaction time task with random response-stimulus intervals (RSIs) between 2 and 10 s, is highly sensitive to behavioral alertness deficits due to sleep loss. To investigate what drives the performance deficits, we conducted an in-laboratory total sleep deprivation (TSD) study and compared performance on the PVT to performance on a 10-min high-density PVT (HD-PVT) with increased stimulus density and truncated RSI range between 2 and 5 s. We hypothesized that the HD-PVT would show greater impairments from TSD than the standard PVT.
Methods: n = 86 healthy adults were randomized (2:1 ratio) to 38 h of TSD (n = 56) or corresponding well-rested control (n = 30). The HD-PVT was administered when subjects had been awake for 34 h (TSD group) or 10 h (control group). Performance on the HD-PVT was compared to performance on the standard PVTs administered 1 h earlier and 1 h later.
Results: The HD-PVT yielded approximately 60% more trials than the standard PVT. The HD-PVT had faster mean response times (RTs) and equivalent lapses (RTs > 500 ms) compared to the standard PVT, with no differences between the TSD effects on mean RT and lapses between tasks. Further, the HD-PVT had a dampened time-on-task effect in both the TSD and control conditions.
Conclusions: Contrary to expectation, the HD-PVT did not show greater performance impairment during TSD, indicating that stimulus density and RSI range are not primary drivers of the PVT's responsiveness to sleep loss.
Sleep loss is common in our 24/7 society with many people routinely sleeping less than they need. Sleep debt is a term describing the difference between the amount of sleep needed, and the amount of sleep obtained. Sleep debt can accumulate over time, resulting in poor cognitive performance, increased sleepiness, poor mood, and a higher risk for accidents. Over the last 30 years, the sleep field has increasingly focused attention on recovery sleep and the ways we can recover from a sleep debt faster and more effectively. While there are still many unanswered questions and debates about the nature of recovery sleep, such as the exact components of sleep important for recovery of function, the amount of sleep needed to recover and the impacts of prior sleep history on recovery, recent research has revealed several important attributes about recovery sleep: (1) the dynamics of the recovery process is impacted by the type of sleep loss (acute versus chronic), (2) mood, sleepiness, and other aspects of cognitive performance recover at different rates, and (3) the recovery process is complex and dependent on the length of recovery sleep and the number of recovery opportunities available. This review will summarize the current state of the literature on recovery sleep, from specific studies of recovery sleep dynamics to napping, "banking" sleep and shiftwork, and will suggest the next steps for research in this field. This paper is part of the David F. Dinges Festschrift Collection. This collection is sponsored by Pulsar Informatics and the Department of Psychiatry in the Perelman School of Medicine at the University of Pennsylvania.
Study objectives: Sleep disruption is a risk factor for obesity, diabetes, and cardiovascular disease in older adults. How physical activity (PA) interacts with the negative cardiometabolic effects of poor sleep is not known. We objectively measured sleep efficiency (SE) in very active older adults and examined the association between SE and a continuous Metabolic Syndrome Risk Score (cMSy).
Methods: Very active older adults (age ≥65 years) from a Master's Ski Team (Whistler, Canada) were recruited. Each participants wore an activity monitor (SenseWear Pro) continuously for 7 days to provide measures of both daily energy expenditure (metabolic equivalents, METs) and SE. All components of the metabolic syndrome were measured and a principal component analysis was used to compute a continuous metabolic risk score (cMSy, sum of eigenvalues ≥1.0).
Results: A total of 54 participants (mean age 71.4 years, SD 4.4 years, and 24 men and 30 women) were recruited and had very high PA levels (>2.5 h per day of exercise). Initially, there was no significant association between SE and cMSy (p = 0.222). When stratified by biological sex, only men showed a significant negative association between SE and cMSy (Standardized β = -0.364 ± 0.159, p = 0.032).
Conclusions: Only older men show a significant negative association between poor SE and increased cardiometabolic risk, despite high levels of PA.
Study objective: We investigated sleep disparities and academic achievement in college.
Methods: Participants were 6,002 first-year college students attending a midsize private university in the southern United States [62.0% female, 18.8% first-generation, 37.4% Black, Indigenous, or People of Color (BIPOC) students]. During the first 3-5 weeks of college, students reported their typical weekday sleep duration, which we classified as short sleep (<7 hours), normal sleep (7-9 hours), or long sleep (>9 hours).
Results: The odds for short sleep were significantly greater in BIPOC students (95% CI: 1.34-1.66) and female students (95% CI: 1.09-1.35), and the odds for long sleep were greater in BIPOC students (95% CI: 1.38-3.08) and first-generation students (95% CI: 1.04-2.53). In adjusted models, financial burden, employment, stress, STEM academic major, student athlete status, and younger age explained unique variance in sleep duration, fully mediating disparities for females and first-generation students (but only partially mediating disparities for BIPOC students). Short and long sleep predicted worse GPA across students' first year in college, even after controlling for high school academic index, demographics, and psychosocial variables.
Conclusions: Higher education should address sleep health early in college to help remove barriers to success and reduce disparities.
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