Performance of wearable sleep trackers during nocturnal sleep and periods of simulated real-world smartphone use.

GOAL AND AIMS: To test sleep/wake transition detection of consumer sleep trackers and research-grade actigraphy during nocturnal sleep and simulated peri-sleep behavior involving minimal movement. FOCUS TECHNOLOGY: Oura Ring Gen 3, Fitbit Sense, AXTRO Fit 3, Xiaomi Mi Band 7, and ActiGraph GT9X. REFERENCE TECHNOLOGY: Polysomnography. SAMPLE: Sixty-three participants (36 female) aged 20-68. DESIGN: Participants engaged in common peri-sleep behavior (reading news articles, watching videos, and exchanging texts) on a smartphone before and after the sleep period. They were woken up during the night to complete a short questionnaire to simulate responding to an incoming message. CORE ANALYTICS: Detection and timing accuracy for the sleep onset times and wake times. ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Discrepancy analysis both including and excluding the peri-sleep activity periods. Epoch-by-epoch analysis of rate and extent of wake misclassification during peri-sleep activity periods. CORE OUTCOMES: Oura and Fitbit were more accurate at detecting sleep/wake transitions than the actigraph and the lower-priced consumer sleep tracker devices. Detection accuracy was less reliable in participants with lower sleep efficiency. IMPORTANT ADDITIONAL OUTCOMES: With inclusion of peri-sleep periods, specificity and Kappa improved significantly for Oura and Fitbit, but not ActiGraph. All devices misclassified motionless wake as sleep to some extent, but this was less prevalent for Oura and Fitbit. CORE CONCLUSIONS: Performance of Oura and Fitbit is robust on nights with suboptimal bedtime routines or minor sleep disturbances. Reduced performance on nights with low sleep efficiency bolsters concerns that these devices are less accurate for fragmented or disturbed sleep.

Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices.

AIMS: Evaluate the performance of 6 wearable sleep trackers across 4 classes (EEG-based headband, research-grade actigraphy, iteratively improved consumer tracker, low-cost consumer tracker). FOCUS TECHNOLOGY: Dreem 3 headband, Actigraph GT9X, Oura Ring Gen3, Fitbit Sense, Xiaomi Mi Band 7, Axtro Fit3. REFERENCE TECHNOLOGY: In-lab polysomnography with 3-reader, consensus sleep scoring. SAMPLE: Sixty participants (26 males) across 3 age groups (18-30, 31-50, and 51-70years). DESIGN: Overnight in a sleep laboratory from habitual sleep time to wake time. CORE ANALYTICS: Discrepancy and epoch-by-epoch analyses for sleep/wake (2-stage) and sleep-stage (4-stage; wake/light/deep/rapid eye movement) classification (devices vs. polysomnography). CORE OUTCOMES: EEG-based Dreem performed the best (2-stage kappa=0.76, 4-stage kappa=0.76-0.86) with the lowest total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset discrepancies vs. polysomnography. This was followed by the iteratively improved consumer trackers: Oura (2-stage kappa=0.64, 4-stage kappa=0.55-0.70) and Fitbit (2-stage kappa=0.58, 4-stage kappa=0.45-0.60) which had comparable total sleep time and sleep efficiency discrepancies that outperformed accelerometry-only Actigraph (2-stage kappa=0.47). The low-cost consumer trackers had poorest overall performance (2-stage kappa<0.31, 4-stage kappa<0.33). IMPORTANT ADDITIONAL OUTCOMES: Proportional biases were driven by nights with poorer sleep (longer sleep onset latencies and/or wake after sleep onset). CORE CONCLUSION: EEG-based Dreem is recommended when evaluating poor quality sleep or when highest accuracy sleep-staging is required. Iteratively improved non-EEG sleep trackers (Oura, Fitbit) balance classification accuracy with well-tolerated, and economic deployment at-scale, and are recommended for studies involving mostly healthy sleepers. The low-cost trackers, can log time in bed but are not recommended for research use.

Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning.

BACKGROUND: Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults. METHODS: Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23-46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP). RESULTS: Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness. CONCLUSION: Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure.

Insights into vascular physiology from sleep photoplethysmography.

STUDY OBJECTIVES: Photoplethysmography (PPG) in consumer sleep trackers is now widely available and used to assess heart rate variability (HRV) for sleep staging. However, PPG waveform changes during sleep can also inform about vascular elasticity in healthy persons who constitute a majority of users. To assess its potential value, we traced the evolution of PPG pulse waveform during sleep alongside measurements of HRV and blood pressure (BP). METHODS: Seventy-eight healthy adults (50% male, median [IQR range] age: 29.5 [23.0, 43.8]) underwent overnight polysomnography (PSG) with fingertip PPG, ambulatory blood pressure monitoring, and electrocardiography (ECG). Selected PPG features that reflect arterial stiffness: systolic to diastolic distance (∆T_norm), normalized rising slope (Rslope) and normalized reflection index (RI) were derived using a custom-built algorithm. Pulse arrival time (PAT) was calculated using ECG and PPG signals. The effect of sleep stage on these measures of arterial elasticity and how this pattern of sleep stage evolution differed with participant age were investigated. RESULTS: BP, heart rate (HR) and PAT were reduced with deeper non-REM sleep but these changes were unaffected by the age range tested. After adjusting for lowered HR, ∆T_norm, Rslope, and RI showed significant effects of sleep stage, whereby deeper sleep was associated with lower arterial stiffness. Age was significantly correlated with the amount of sleep-related change in ∆T_norm, Rslope, and RI, and remained a significant predictor of RI after adjustment for sex, body mass index, office BP, and sleep efficiency. CONCLUSIONS: The current findings indicate that the magnitude of sleep-related change in PPG waveform can provide useful information about vascular elasticity and age effects on this in healthy adults.

Advantage conferred by overnight sleep on schema-related memory may last only a day.

Study Objectives: Sleep contributes to declarative memory consolidation. Independently, schemas benefit memory. Here we investigated how sleep compared with active wake benefits schema consolidation 12 and 24 hours after initial learning. Methods: Fifty-three adolescents (age: 15-19 years) randomly assigned into sleep and active wake groups participated in a schema-learning protocol based on transitive inference (i.e. If B > C and C > D then B > D). Participants were tested immediately after learning and following 12-, and 24-hour intervals of wake or sleep for both the adjacent (e.g. B-C, C-D; relational memory) and inference pairs: (e.g.: B-D, B-E, and C-E). Memory performance following the respective 12- and 24-hour intervals were analyzed using a mixed ANOVA with schema (schema, no-schema) as the within-participant factor, and condition (sleep, wake) as the between-participant factor. Results: Twelve hours after learning, there were significant main effects of condition (sleep, wake) and schema, as well as a significant interaction, whereby schema-related memory was significantly better in the sleep condition compared to wake. Higher sleep spindle density was most consistently associated with greater overnight schema-related memory benefit. After 24 hours, the memory advantage of initial sleep was diminished. Conclusions: Overnight sleep preferentially benefits schema-related memory consolidation following initial learning compared with active wake, but this advantage may be eroded after a subsequent night of sleep. This is possibly due to delayed consolidation that might occur during subsequent sleep opportunities in the wake group. Clinical Trial Information: Name: Investigating Preferred Nap Schedules for Adolescents (NFS5) URL: https://clinicaltrials.gov/ct2/show/NCT04044885. Registration: NCT04044885.

Working-from-home persistently influences sleep and physical activity 2 years after the Covid-19 pandemic onset: a longitudinal sleep tracker and electronic diary-based study.

Objective: Working from home (WFH) has become common place since the Covid-19 pandemic. Early studies observed population-level shifts in sleep patterns (later and longer sleep) and physical activity (reduced PA), during home confinement. Other studies found these changes to depend on the proportion of days that individuals WFH (vs. work from office; WFO). Here, we examined the effects of WFH on sleep and activity patterns in the transition to normality during the later stages of the Covid-19 pandemic (Aug 2021-Jan 2022). Methods: Two-hundred and twenty-five working adults enrolled in a public health study were followed for 22 weeks. Sleep and activity data were collected with a consumer fitness tracker (Fitbit Versa 2). Over three 2-week periods (Phase 1/week 1-2: August 16-29, 2021; Phase 2/week 11-12: October 25-November 7, 2021; Phase 3/week 21-22: January 3-16, 2022), participants provided daily Fitbit sleep and activity records. Additionally, they completed daily phone-based ecological momentary assessment (EMA), providing ratings of sleep quality, wellbeing (mood, stress, motivation), and information on daily work arrangements (WFH, WFO, no work). Work arrangement data were used to examine the effects of WFH vs. WFO on sleep, activity, and wellbeing. Results: The proportion of WFH vs. WFO days fluctuated over the three measurement periods, mirroring evolving Covid restrictions. Across all three measurement periods WFH days were robustly associated with later bedtimes (+14.7 min), later wake times (+42.3 min), and longer Total Sleep Time (+20.2 min), compared to WFO days. Sleep efficiency was not affected. WFH was further associated with lower daily step count than WFO (-2,471 steps/day). WFH was associated with higher wellbeing ratings compared to WFO for those participants who had no children. However, for participants with children, these differences were not present. Conclusion: Pandemic-initiated changes in sleep and physical activity were sustained during the later stage of the pandemic. These changes could have longer term effects, and conscious effort is encouraged to harness the benefits (i.e., longer sleep), and mitigate the pitfalls (i.e., less physical activity). These findings are relevant for public health as hybrid WHF is likely to persist in a post-pandemic world.

Influence of mid-afternoon nap duration and sleep parameters on memory encoding, mood, processing speed, and vigilance.

STUDY OBJECTIVES: To determine how mid-afternoon naps of differing durations benefit memory encoding, vigilance, speed of processing (SOP), mood, and sleepiness; to evaluate if these benefits extend past 3 hr post-awakening and to examine how sleep macrostructure during naps modulate these benefits. METHODS: Following short habitual sleep, 32 young adults underwent four experimental conditions in randomized order: wake; naps of 10 min, 30 min, and 60 min duration verified with polysomnography. A 10-min test battery was delivered at a pre-nap baseline, and at 5 min, 30 min, 60 min, and 240 min post-nap. Participants encoded pictures 90 min post-nap and were tested for recognition 210 min later. RESULTS: Naps ranging from 10 to 60 min increased positive mood and alleviated self-reported sleepiness up to 240 min post-nap. Compared to waking, only naps of 30 min improved memory encoding. Improvements in vigilance were moderate, and benefits for SOP were not observed. Sleep inertia was observed for the 30 min to 60 min naps but was resolved within 30 min after waking. We found no significant associations between sleep macrostructure and memory benefits. CONCLUSIONS: With short habitual sleep, naps ranging from 10 to 60 min had clear and lasting benefits for positive mood and self-reported sleepiness/alertness. Cognitive improvements were moderate, with only the 30 min nap showing benefits for memory encoding. While there is no clear "winning" nap duration, a 30 min nap appears to have the best trade-off between practicability and benefit. CLINICAL TRIAL ID: Effects of Varying Duration of Naps on Cognitive Performance and Memory Encoding, https://www.clinicaltrials.gov/ct2/show/NCT04984824, NCT04984824.

Adherence to 24-Hour Movement Recommendations and Health Indicators in Early Adolescence: Cross-Sectional and Longitudinal Associations in the Adolescent Brain Cognitive Development Study.

PURPOSE: Adherence to 24-hour movement guidelines of ≥60 minutes of physical activity, ≤2 hours of screen time, and 9-11 hours of sleep has been shown to benefit cognitive, physical, and psychosocial health in children and young adolescents aged 5-13 years. However, these findings have mostly been based on cross-sectional studies or relatively small samples and the associations between adherence to guidelines and brain structure remain to be evaluated. METHODS: Data from the Adolescent Brain Cognitive Development℠ (ABCD) study of 10,574 early adolescents aged 9-14 years from September 2016 to January 2021 were used to examine whether adherence to 24-hour movement guidelines benefits cognition (general cognitive ability, executive function, and learning/memory assessed by the National Institutes of Health Toolbox neurocognitive battery), body mass index, psychosocial health (internalizing, externalizing, and total problems from the parent-reported Child Behavior Checklist), and magnetic resonance imaging-derived brain morphometric measures at baseline (T1), ∼2 years later (T2), and longitudinally from T1 to T2 (T2-T1). Multivariable linear mixed models were used, with adjustments for sociodemographic confounders. Time elapsed and T1 outcome measures were also controlled for in longitudinal models. RESULTS: Better cognitive scores, fewer behavioral problems, lower adiposity levels, and greater gray matter volumes were observed in those who met both sleep and screen time recommendations compared to those who met none. Longitudinal follow-up further supports these findings; participants who met both recommendations at T1 and T2 evidenced better outcome measures than those who met none. DISCUSSION: These findings support consideration of integrated rather than isolated movement recommendations across the day in early adolescence for better cognitive, physical and psychosocial health. Although the associations between physical activity and health indicators were less consistent in this study, the significant findings from sleep and screen time demonstrate the importance of considering movement recommendations in an integrated rather than isolated manner for adolescent health. It is recommended that movement behaviors be simultaneously targeted for better developmental outcomes.

Associations between objectively measured sleep parameters and cognition in healthy older adults: A meta-analysis.

Multiple studies have examined associations between sleep and cognition in older adults, but a majority of these depend on self-reports on sleep and utilize cognitive tests that assess overall cognitive function. The current meta-analysis involved 72 independent studies and sought to quantify associations between objectively measured sleep parameters and cognitive performance in healthy older adults. Both sleep macrostructure (e.g., sleep duration, continuity, and stages) and microstructure (e.g., slow wave activity and spindle activity) were evaluated. For macrostructure, lower restlessness at night was associated with better memory performance (r = 0.43, p = 0.02), while lower sleep onset latency was associated with better executive functioning (r = 0.28, p = 0.03). Greater relative amount of N2 and REM sleep, but not N3, positively correlated with cognitive performance. The association between microstructure and cognition in older adults was marginally significant. This relationship was moderated by age (z = 0.07, p < 0.01), education (z = 0.26, p = 0.03), and percentage of female participants (z = 0.01, p < 0.01). The current meta-analysis emphasizes the importance of considering objective sleep measures to understand the relationship between sleep and cognition in healthy older adults. These results also form a base from which researchers using wearable sleep technology and measuring behavior through computerized testing tools can evaluate their findings.

A randomized-controlled trial of a digital, small incentive-based intervention for working adults with short sleep.

STUDY OBJECTIVES: We evaluated the efficacy of a digitally delivered, small and scalable incentive-based intervention program on sleep and wellbeing in short-sleeping, working adults. METHODS: A 22-week, parallel-group, randomized-controlled trial was conducted on 21-40 y participants gifted with FitbitTM devices to measure sleep for ≥2 years, as part of a broader healthy lifestyle study. About 225 short sleepers (141 males; average time-in-bed, TIB < 7h) were randomly assigned in a 2:1 ratio to Goal-Setting or Control groups. The Goal-Setting group received health vouchers (~USD 0.24) for meeting each sleep goal (i.e. increasing weeknight TIB by 30 min/sleeping before midnight).The study spanned three phases: (1) 2-week Baseline, (2) 10-week Intervention, and (3) 10-week Follow-Up. Wellbeing questionnaires were administered on Weeks 1-2, 11-12, and 21-22. RESULTS: Baseline weeknight TIB (mean ±â€…SD) was 387 ±â€…43 min (Goal-Setting) and 399 ±â€…44 min (Control), while bedtime was 00:53 ±â€…01:13 (Goal-Setting), and 00:38 ±â€…00:56 (Control). No difference in sleep outcomes was observed at study endpoints, but exploratory week-by-week analysis showed that on Weeks 3-5, TIB in the Goal-Setting group increased (9-18 min; ps < 0.05) while on Week 5, bedtimes shifted earlier (15 min; p < 0.01) compared to Baseline. Morning sleepiness was reduced in the Goal-Setting group (mean[SEM] = -3.17(1.53); p = 0.04) compared to Baseline, although between-group differences were not significant (p = 0.62). Main barriers to sleeping longer were work hours (35%), followed by leisure activities (23%) and family commitments (22%). CONCLUSION: Our program resulted in encouraging subjective sleep improvements and short-term sleep extension, but sustained transformation of sleep will probably require structural measures to overcome significant obstacles to sleep. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04878380 (hiSG Sleep Health Study (hiSG-SHS); https://clinicaltrials.gov/ct2/show/NCT04878380).

Mindfulness-based therapy for insomnia for older adults with sleep difficulties: a randomized clinical trial.

OBJECTIVE: Poor sleep is a modifiable risk factor for multiple disorders. Frontline treatments (e.g. cognitive-behavioral therapy for insomnia) have limitations, prompting a search for alternative approaches. Here, we compare manualized Mindfulness-Based Therapy for Insomnia (MBTI) with a Sleep Hygiene, Education, and Exercise Program (SHEEP) in improving subjective and objective sleep outcomes in older adults. METHODS: We conducted a single-site, parallel-arm trial, with blinded assessments collected at baseline, post-intervention and 6-months follow-up. We randomized 127 participants aged 50-80, with a Pittsburgh Sleep Quality Index (PSQI) score ⩾5, to either MBTI (n = 65) or SHEEP (n = 62), both 2 hr weekly group sessions lasting 8 weeks. Primary outcomes included PSQI and Insomnia Severity Index, and actigraphy- and polysomnography-measured sleep onset latency (SOL) and wake after sleep onset (WASO). RESULTS: Intention-to-treat analysis showed reductions in insomnia severity in both groups [MBTI: Cohen's effect size d = -1.27, 95% confidence interval (CI) -1.61 to -0.89; SHEEP: d = -0.69, 95% CI -0.96 to -0.43], with significantly greater improvement in MBTI. Sleep quality improved equivalently in both groups (MBTI: d = -1.19; SHEEP: d = -1.02). No significant interaction effects were observed in objective sleep measures. However, only MBTI had reduced WASOactigraphy (MBTI: d = -0.30; SHEEP: d = 0.02), SOLactigraphy (MBTI: d = -0.25; SHEEP: d = -0.09), and WASOPSG (MBTI: d = -0.26; SHEEP (d = -0.18). There was no change in SOLPSG. No participants withdrew because of adverse effects. CONCLUSIONS: MBTI is effective at improving subjective and objective sleep quality in older adults, and could be a valid alternative for persons who have failed or do not have access to standard frontline therapies.

An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device.

Background: The rapid advancement in wearable solutions to monitor and score sleep staging has enabled monitoring outside of the conventional clinical settings. However, most of the devices and algorithms lack extensive and independent validation, a fundamental step to ensure robustness, stability, and replicability of the results beyond the training and testing phases. These systems are thought not to be feasible and reliable alternatives to the gold standard, polysomnography (PSG). Materials and methods: This validation study highlights the accuracy and precision of the proposed heart rate (HR)-based deep-learning algorithm for sleep staging. The illustrated solution can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-s epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n = 994 participants, 994 recordings) and a proprietary dataset of ECG recordings (Z3Pulse, n = 52 participants, 112 recordings) collected with a chest-worn, wireless sensor and simultaneous PSG collection using SOMNOtouch. Results: We evaluated the performance of the models in both datasets in terms of Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP), Positive Predictive Value (PPV), and Negative Predicted Value (NPV). In the CinC dataset, the highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect scoring, while a significant decrease of performance by age was reported across the models. In the Z3Pulse dataset, the highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment. Conclusion: The results of the validation procedure demonstrated the feasibility of accurate HR-based sleep staging. The combination of the proposed sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution deployable in the home environment and robust across age, sex, and AHI scores.

Multi-Night at-Home Evaluation of Improved Sleep Detection and Classification with a Memory-Enhanced Consumer Sleep Tracker.

Purpose: To evaluate the benefits of applying an improved sleep detection and staging algorithm on minimally processed multi-sensor wearable data collected from older generation hardware. Patients and Methods: 58 healthy, East Asian adults aged 23-69 years (M = 37.10, SD = 13.03, 32 males), each underwent 3 nights of PSG at home, wearing 2nd Generation Oura Rings equipped with additional memory to store raw data from accelerometer, infra-red photoplethysmography and temperature sensors. 2-stage and 4-stage sleep classifications using a new machine-learning algorithm (Gen3) trained on a diverse and independent dataset were compared to the existing consumer algorithm (Gen2) for whole-night and epoch-by-epoch metrics. Results: Gen 3 outperformed its predecessor with a mean (SD) accuracy of 92.6% (0.04), sensitivity of 94.9% (0.03), and specificity of 78.5% (0.11); corresponding to a 3%, 2.8% and 6.2% improvement from Gen2 across the three nights, with Cohen's d values >0.39, t values >2.69, and p values <0.01. Notably, Gen 3 showed robust performance comparable to PSG in its assessment of sleep latency, light sleep, rapid eye movement (REM), and wake after sleep onset (WASO) duration. Participants <40 years of age benefited more from the upgrade with less measurement bias for total sleep time (TST), WASO, light sleep and sleep efficiency compared to those ≥40 years. Males showed greater improvements on TST and REM sleep measurement bias compared to females, while females benefitted more for deep sleep measures compared to males. Conclusion: These results affirm the benefits of applying machine learning and a diverse training dataset to improve sleep measurement of a consumer wearable device. Importantly, collecting raw data with appropriate hardware allows for future advancements in algorithm development or sleep physiology to be retrospectively applied to enhance the value of longitudinal sleep studies.

Dissociable changes in sleep architecture with mindfulness and sleep hygiene intervention in older adults: Secondary and exploratory analysis of polysomnography data from the Mindfulness Sleep Therapy (MIST) trial.

OBJECTIVES: We conducted a secondary analysis of the Mindfulness Sleep Therapy study, a randomized controlled trial testing Mindfulness-Based Therapy for Insomnia (MBTI) against a sleep hygiene education and exercise program (SHEEP). We investigated whether the interventions led to changes in sleep macroarchitecture (N2, N3 and REM), and microarchitecture (sleep fragmentation, slow wave activity, spectral band power) measured by ambulatory polysomnography (PSG). METHODS: 48 MBTI and 46 SHEEP participants provided usable PSG and subjective sleep quality data both pre- and post intervention. The interventions consisted of 8 weekly 2-hour group sessions, and daily practice. PSG data were staged according to the American Academy of Sleep Medicine criteria by 2 technicians blind to time point and condition. Repeated-measures ANOVA and permutation analysis were used to test for differences over time and between the interventions. RESULTS: Self-reported sleep quality improved in both study groups. We observed significant increases in N2 in MBTI but not SHEEP (p = .045), and significant increases in N3 in SHEEP but not MBTI (p = .012). No significant differences over time or between group were observed in N1, REM, or sleep fragmentation. Higher frequency non-REM EEG power decreased in SHEEP but not MBTI. Slow wave activity and slow wave activity dissipation did not differ over time or between groups. Among all variables, significant time by group interactions were observed in only N3 and non-REM alpha power. CONCLUSIONS: MBTI and sleep hygiene education had different effects on sleep macro and microarchitecture, suggesting that the underlying mechanisms of mindfulness training in improving sleep quality may differ from traditional interventions.

A sleep schedule incorporating naps benefits the transformation of hierarchical knowledge.

STUDY OBJECTIVES: The learning brain establishes schemas (knowledge structures) that benefit subsequent learning. We investigated how sleep and having a schema might benefit initial learning followed by rearranged and expanded memoranda. We concurrently examined the contributions of sleep spindles and slow-wave sleep to learning outcomes. METHODS: Fifty-three adolescents were randomly assigned to an 8 h Nap schedule (6.5 h nocturnal sleep with a 90-minute daytime nap) or an 8 h No-Nap, nocturnal-only sleep schedule. The study spanned 14 nights, simulating successive school weeks. We utilized a transitive inference task involving hierarchically ordered faces. Initial learning to set up the schema was followed by rearrangement of the hierarchy (accommodation) and hierarchy expansion (assimilation). The expanded sequence was restudied. Recall of hierarchical knowledge was tested after initial learning and at multiple points for all subsequent phases. As a control, both groups underwent a No-schema condition where the hierarchy was introduced and modified without opportunity to set up a schema. Electroencephalography accompanied the multiple sleep opportunities. RESULTS: There were main effects of Nap schedule and Schema condition evidenced by superior recall of initial learning, reordered and expanded memoranda. Improved recall was consistently associated with higher fast spindle density but not slow-wave measures. This was true for both nocturnal sleep and daytime naps. CONCLUSION: A sleep schedule incorporating regular nap opportunities compared to one that only had nocturnal sleep benefited building of robust and flexible schemas, facilitating recall of the subsequently rearranged and expanded structured knowledge. These benefits appear to be strongly associated with fast spindles. CLINICAL TRIAL REGISTRATION: NCT04044885 (https://clinicaltrials.gov/ct2/show/NCT04044885).

Staying vigilant during recurrent sleep restriction: dose-response effects of time-in-bed and benefits of daytime napping.

STUDY OBJECTIVES: We characterized vigilance deterioration with increasing time-on-task (ToT) during recurrent sleep restriction of different extents on simulated weekdays and recovery sleep on weekends, and tested the effectiveness of afternoon napping in ameliorating ToT-related deficits. METHODS: In the Need for Sleep studies, 194 adolescents (age = 15-19 years) underwent two baseline nights of 9-h time-in-bed (TIB), followed by two cycles of weekday manipulation nights and weekend recovery nights (9-h TIB). They were allocated 9 h, 8 h, 6.5 h, or 5 h of TIB for nocturnal sleep on weekdays. Three additional groups with 5 h or 6.5 h TIB were given an afternoon nap opportunity (5 h + 1 h, 5 h + 1.5 h, and 6.5 h + 1.5 h). ToT effects were quantified by performance change from the first 2 min to the last 2 min in a 10-min Psychomotor Vigilance Task administered daily. RESULTS: The 9 h and the 8 h groups showed comparable ToT effects that remained at baseline levels throughout the protocol. ToT-related deficits were greater among the 5 h and the 6.5 h groups, increased prominently in the second week of sleep restriction despite partial recuperation during the intervening recovery period and diverged between these two groups from the fifth sleep-restricted night. Daytime napping attenuated ToT effects when nocturnal sleep restriction was severe (i.e. 5-h TIB/night), and held steady at baseline levels for a milder dose of nocturnal sleep restriction when total TIB across 24 h was within the age-specific recommended sleep duration (i.e. 6.5 h + 1.5 h). CONCLUSIONS: Reducing TIB beyond the recommended duration significantly increases ToT-associated vigilance impairment, particularly during recurrent periods of sleep restriction. Daytime napping is effective in ameliorating such decrement. CLINICAL TRIAL REGISTRATION: NCT02838095, NCT03333512, and NCT04044885.

Reopening after lockdown: the influence of working-from-home and digital device use on sleep, physical activity, and wellbeing following COVID-19 lockdown and reopening.

STUDY OBJECTIVES: COVID-19 lockdowns drastically affected sleep, physical activity, and wellbeing. We studied how these behaviors evolved during reopening the possible contributions of continued working from home and smartphone usage. METHODS: Participants (N = 198) were studied through the lockdown and subsequent reopening period, using a wearable sleep/activity tracker, smartphone-delivered ecological momentary assessment (EMA), and passive smartphone usage tracking. Work/study location was obtained through daily EMA ascertainment. RESULTS: Upon reopening, earlier, shorter sleep and increased physical activity were observed, alongside increased self-rated stress and poorer evening mood ratings. These reopening changes were affected by post-lockdown work arrangements and patterns of smartphone usage. Individuals who returned to work or school in-person tended toward larger shifts to earlier sleep and wake timings. Returning to in-person work/school also correlated with more physical activity. Contrary to expectation, there was no decrease in objectively measured smartphone usage after reopening. A cluster analysis showed that persons with relatively heavier smartphone use prior to bedtime had later sleep timings and lower physical activity. CONCLUSIONS: These observations indicate that the reopening after lockdown was accompanied by earlier sleep timing, increased physical activity, and altered mental wellbeing. Moreover, these changes were affected by work/study arrangements and smartphone usage patterns.

Cortical thinning and sleep slow wave activity reductions mediate age-related improvements in cognition during mid-late adolescence.

STUDY OBJECTIVES: Gains in cognitive test performance that occur during adolescence are associated with brain maturation. Cortical thinning and reduced sleep slow wave activity (SWA) are markers of such developmental changes. Here we investigate whether they mediate age-related improvements in cognition. METHODS: 109 adolescents aged 15-19 years (49 males) underwent magnetic resonance imaging, polysomnography (PSG), and a battery of cognitive tasks within a 2-month time window. Cognitive tasks assessed nonverbal intelligence, sustained attention, speed of processing and working memory and executive function. To minimize the effect of sleep history on SWA and cognitive performance, PSG and test batteries were administered only after at least 8 nights of 9-h time-in-bed (TIB) sleep opportunity. RESULTS: Age-related improvements in speed of processing (r = 0.33, p = 0.001) and nonverbal intelligence (r = 0.24, p = 0.01) domains were observed. These cognitive changes were associated with reduced cortical thickness, particularly in bilateral temporoparietal regions (rs = -0.21 to -0.45, ps < 0.05), as well as SWA (r = -0.35, p < 0.001). Serial mediation models found that ROIs in the middle/superior temporal cortices, together with SWA mediated the age-related improvement observed on cognition. CONCLUSIONS: During adolescence, age-related improvements in cognition are mediated by reductions in cortical thickness and sleep SWA.

Predicting vigilance vulnerability during 1 and 2 weeks of sleep restriction with baseline performance metrics.

Study Objectives: We attempted to predict vigilance performance in adolescents during partial sleep deprivation using task summary metrics and drift diffusion modelling measures (DDM) derived from baseline vigilance performance. Methods: In the Need for Sleep studies, 57 adolescents (age = 15-19 years) underwent two baseline nights of 9-h time-in-bed (TIB), followed by two cycles of weekday sleep-restricted nights (5-h or 6.5-h TIB) and weekend recovery nights (9-h TIB). Vigilance was assessed daily with the Psychomotor Vigilance Task (PVT), with the number of lapses (response times ≥ 500 ms) as the primary outcome measure. The two DDM predictors were drift rate, which quantifies the speed of information accumulation and determines how quickly an individual derives a decision response, and non-decision time range, which indicates within-subject variation in physical, non-cognitive responding, e.g. motor actions. Results: In the first week of sleep curtailment, faster accumulation of lapses was significantly associated with more lapses at baseline (p = .02), but not the two baseline DDM metrics: drift and non-decision time range (p > .07). On the other hand, faster accumulation of lapses and greater increment in reaction time variability from the first to the second week of sleep restriction were associated with lower drift (p < .007) at baseline. Conclusions: Among adolescents, baseline PVT lapses can predict inter-individual differences in vigilance vulnerability during 1 week of sleep restriction on weekdays, while drift more consistently predicts vulnerability during more weeks of sleep curtailment. Clinical Trial Information: Effects of Napping in Sleep-Restricted Adolescents, clinicaltrials.gov, NCT02838095. The Cognitive and Metabolic Effects of Sleep Restriction in Adolescents (NFS4), clinicaltrials.gov, NCT03333512.